WO2014061604A1 - Maintenance-plan-drafting support system, maintenance-plan-drafting support method, and maintenance-plan-drafting support program - Google Patents

Maintenance-plan-drafting support system, maintenance-plan-drafting support method, and maintenance-plan-drafting support program Download PDF

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Publication number
WO2014061604A1
WO2014061604A1 PCT/JP2013/077813 JP2013077813W WO2014061604A1 WO 2014061604 A1 WO2014061604 A1 WO 2014061604A1 JP 2013077813 W JP2013077813 W JP 2013077813W WO 2014061604 A1 WO2014061604 A1 WO 2014061604A1
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Prior art keywords
maintenance
information
database
parts
work
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PCT/JP2013/077813
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French (fr)
Japanese (ja)
Inventor
峻行 羽渕
裕 吉川
延之 太田
荒井　正人
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株式会社日立製作所
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Application filed by 株式会社日立製作所 filed Critical 株式会社日立製作所
Priority to JP2014542114A priority Critical patent/JP5938481B6/en
Priority to AU2013332924A priority patent/AU2013332924B2/en
Priority to CA2888334A priority patent/CA2888334C/en
Publication of WO2014061604A1 publication Critical patent/WO2014061604A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance

Definitions

  • the present invention relates to a maintenance planning support system, a maintenance planning support method, and a maintenance planning support program.
  • an object of the present invention is to provide a technique that enables an effective maintenance plan to be efficiently formulated without depending on the skills of maintenance personnel.
  • the maintenance planning support system of the present invention that solves the above problems includes information on a phenomenon that has occurred in the work machine, information on a failure that has occurred in the work machine after the occurrence of the phenomenon, and maintenance work that has been performed for the failure.
  • a first database that holds information in association with each other
  • a second database that holds information on standard maintenance work defined for each work machine, and information on the availability of each person and equipment for maintenance work
  • the third database to be held, each stock and price of parts used for maintenance work or its old version parts or remanufactured parts, and delivery date and transportation cost information for each transportation destination and transportation means are retained for each warehouse.
  • the first database is used to collate the information on the phenomenon, the failure that may occur after the occurrence of the corresponding phenomenon is estimated, and the information on the maintenance work performed on the work machine at the time of the failure is identified in the first database.
  • the maintenance work information is collated with the second database, the standard maintenance work information expected to be carried out on the work machine is specified, and the maintenance work personnel and equipment availability specified by the maintenance work information Is identified in the third database as the maintenance implementation candidate date, and the parts specified by the maintenance work information or its old version parts or remanufactured parts are in stock, and the parts or their old version parts or regenerated parts are transported to the location of the work machine
  • the delivery date for delivery by means is within the grace period from the current time to the maintenance candidate date
  • the transportation means corresponding to the delivery date and its transportation cost Generate and output information on delivery dates, warehouses, transportation means, transportation costs and maintenance candidate dates as maintenance plan information for the parts to be used for maintenance work or the old version parts or recycled parts specified in the database.
  • a processing device that executes processing to be output to the device.
  • FIG. 1 is a diagram showing a network configuration example including a maintenance plan planning system 100 according to the present embodiment.
  • a maintenance plan planning system 100 shown in FIG. 1 is a computer system that enables an effective maintenance plan to be efficiently planned without depending on the skills of maintenance personnel.
  • a large mining machine will be described as an example of a work machine.
  • the market for this mining machine has expanded rapidly due to the increase in global demand for resources, while the parts supply capacity on the machine manufacturer side has not caught up with the market demand.
  • new parts that are the latest version, old version parts that have not been retroactively implemented, and refurbished parts that have repaired and refurbished faulty parts can be used in common.
  • Skilled maintenance staff used various parts with the same functions but different states and origins. However, parts with such various attributes are stored in warehouses scattered around the world, and unskilled maintenance personnel accurately recognize attributes, locations, inventory, etc., and appropriately incorporate them into the mining equipment maintenance plan. Has been difficult in the past.
  • the maintenance plan planning support system 100 according to the present embodiment enables even an inexperienced maintenance staff to appropriately adopt the above-described components having various attributes and appropriately plan a complicated maintenance plan.
  • the maintenance planning support system 100 is connected to the network 180 and can communicate data with the monitoring system 170 and the client terminal 190.
  • the monitoring system 170 monitors the measurement value of the sensor 11 installed in the mining machine 10, that is, sensor data, or monitors the parameter calculated by applying the sensor data to an appropriate algorithm, and 10 is a computer system for detecting an abnormality in 10.
  • the monitoring system 170 detects an abnormality when the sensor data or the calculated parameter exceeds a predetermined threshold, and transmits a notification of the abnormality detection to the maintenance planning support system 100.
  • the sensor 11 described above can be assumed to be a sensor that measures, for example, the motor rotation speed of the mining machine 10, the pump internal pressure, and the temperature and vibration of each place.
  • the sensor 11 installed in the mining machine 10 transmits sensor data to the monitoring system 170 by using a communication device provided in the mining machine 10 or a communication device provided in the sensor 11 itself.
  • the client terminal 190 accesses the maintenance plan planning support system 100, receives data input from maintenance personnel using a keyboard, a mouse, and the like, or displays data obtained from the maintenance plan planning support system 100 on a display or the like. It is responsible for each processing.
  • the hardware configuration of the maintenance planning system 100 is as follows.
  • the maintenance planning system 100 reads out to the memory 113 a storage device 115 composed of a suitable non-volatile storage device such as a hard disk drive, a memory 113 composed of a volatile storage device such as RAM, and a program held in the storage device 115.
  • the CPU 114 (arithmetic unit) for performing overall control of the system itself and performing various determinations, computations and control processes, and the communication device 112 connected to the network 180 and responsible for communication processing with other devices.
  • Functions implemented by executing the above-described programs include a phenomenon diagnosis function 121, a failure diagnosis function 122, a countermeasure extraction function 131, a plan creation function 141, a periodic maintenance plan adjustment function 142, a life calculation function 151, and a downtime calculation function 152.
  • a functional unit in which each of these function groups and a database group storing data used by each function is illustrated.
  • the functional units include an abnormality diagnosis unit 120, a countermeasure extraction unit 130, a plan creation unit 140, a performance prediction unit 150, and an output management unit 160. Data transmission between the functional units is managed by the I / O 111 via the BUS.
  • the database of each functional unit will be described later.
  • the maintenance planning support system 100 may have an input / output function and a device (display, keyboard, etc.). .
  • the maintenance planning support system 100 receives information on a phenomenon that has occurred in the mining machine 10 at a certain location from the monitoring system 170 via the network 180, and uses the phenomenon history database 123 and the failure history database as information on the received phenomenon. 124 (both are the first database), a failure that may occur after the occurrence of the corresponding phenomenon is estimated, and information on the maintenance work performed on the mining machine 10 at the time of the corresponding failure is stored in the work history database 132 (first database). Specific functions.
  • the maintenance planning support system 100 collates the maintenance work information specified above with the maintenance work database 133 (second database), and standard maintenance work expected to be performed on the mining machine 10 described above. And the maintenance work candidate specified by the maintenance work information and the time when the equipment can be operated are specified in the schedule database 143 (third database) as the maintenance execution candidate date.
  • the maintenance planning support system 100 stocks the parts specified by the above-described maintenance work information or the old version parts or the recycled parts thereof, and transports the corresponding parts or the old version parts or the recycled parts to the location of the corresponding mining machine 10.
  • the maintenance planning support system 100 uses the customer knowledge database 145 (No. 1) as the load factor information of the mining machine 10 or the part where the phenomenon occurs, which is included in the information on the phenomenon related to the mining machine 10 (from the monitoring system 170). 5 database), estimated the remaining life of the mining machine 10 or the part where the phenomenon occurred, and reduced the load factor according to the extent that the remaining life is less than the grace period from the current time to the maintenance candidate date. Is the load factor in the corresponding mining machine 10, the economic loss at the load factor is specified in the customer knowledge database 145 (fifth database), and the reduced load factor and the information on the economic loss at the load factor are described above. It is included in the maintenance plan information included in the maintenance plan database 161 or output to the client terminal 190.
  • the maintenance planning support system 100 relates to a part used for maintenance work or an old version part or a reproduced part indicated by the above-described maintenance plan information, in the part history database 153 (sixth database), the same type of part or an old version thereof.
  • the history of the attachment and removal of the part or the remanufactured part is specified, the time between the specified attachment and removal is calculated as the life, and the life information is included in the maintenance plan information and stored in the maintenance plan database 161 Or a function of outputting to the client terminal 190.
  • the maintenance planning support system 100 relates to a part used for maintenance work or an old version part or a reproduction part thereof indicated by the above-described maintenance plan information, and the same type of part or an old version thereof in the failure history database 124 (first database).
  • the presence or absence of failure information on the part or the remanufactured part is specified, and when the failure information exists, the maintenance work information performed for the failure is specified in the work history database 132 (first database).
  • the time from the start of work to the end of work indicated by the information of the corresponding maintenance work is calculated as downtime, and the downtime information is included in the maintenance plan information described above and stored in the maintenance plan database 161, Alternatively, it has a function of outputting to the client terminal 190.
  • the maintenance planning support system 100 relates to a part used for maintenance work or an old version part or a recycled part indicated by the maintenance plan information described above, in the operation result table 1010 (seventh database) of the part operation database 154.
  • Information on the measured value of the part or its old version part or recycled part is specified, and the information on the specified measured value is collated with the operation determination table 1000 (eighth database) of the part operation database 154, It has the function of calculating the downtime of the old version parts or the recycled parts as downtime and storing the downtime information in the maintenance plan database 161 included in the above-mentioned maintenance plan information or outputting it to the client terminal 190 .
  • the maintenance plan formulation support system 100 determines whether the regular maintenance schedule is included in the regular maintenance database 146 (the ninth database) within the period from the present time to the maintenance execution candidate date indicated by the above-described maintenance plan information. If the scheduled maintenance schedule is included in the period until the maintenance execution candidate date indicated by the maintenance plan information, replace the maintenance execution candidate date with the corresponding scheduled maintenance date, and generate maintenance plan information again. It has the function to do.
  • the abnormality diagnosis unit 120 included in the maintenance planning support system 100 includes a phenomenon history database 122 that stores phenomenon histories that have occurred in the mining machine 10 and the like, and sensor data or parameters received from the monitoring system 170 by the phenomenon diagnosis function 121. By referring to and comparing, it is diagnosed what kind of phenomenon the abnormality detected by the monitoring system 170 for the mining machine 10 is.
  • the abnormality diagnosis unit 120 included in the maintenance planning support system 100 includes a failure history database 124 that stores failure histories that have occurred in the mining machine 10 and the like using the failure diagnosis function 122, and the phenomenon that the phenomenon diagnosis function 121 identifies. By referring to and comparing the above, it is diagnosed what kind of failure the phenomenon specified by the phenomenon diagnosis function 121 is.
  • the abnormality diagnosis unit 120 of the maintenance planning support system 100 identifies what kind of failure the abnormality detected by the monitoring system 170 is associated with using the above-described function group.
  • the phenomenon diagnosis function 121 and the failure diagnosis function 122 of the abnormality diagnosis unit 120 transmit the specified phenomenon and failure to the output management unit 160.
  • the output management unit 160 stores and manages this in the maintenance plan database 161.
  • the countermeasure extraction unit 130 included in the maintenance planning support system 100 uses the countermeasure extraction function 131 to identify the work history database 132 in which maintenance work histories for past failures of the mining machine 10 and the like and the failure diagnosis function 122 have identified. By comparing the failure with the reference, a countermeasure to be taken is extracted for the abnormality detected by the monitoring system 170. Further, the countermeasure extracting function 131 refers to the maintenance work database 133 and extracts maintenance staff skills, equipment, work costs, and work time necessary for performing the maintenance work extracted from the work history database 132. In addition, the measure extraction function 131 transmits information on measures to be taken, extracted as described above, to the output management unit 160. The output management unit 160 stores and manages this in the maintenance plan database 161.
  • the plan creation unit 140 included in the maintenance plan planning support system 100 extracts a schedule of maintenance personnel and equipment corresponding to the measures extracted by the above-described measure extraction unit 130 from the schedule database 143 by the plan creation function 141. Extract maintenance candidate dates.
  • the plan creation function 141 refers to the parts procurement database 144 and drafts a procurement plan for procurement. Details of this planning procedure will be described later. Further, the plan creation function 141 drafts an operation plan from the operation loss information and the remaining life information in the customer knowledge database 145. Details of this planning procedure will be described later.
  • the regular maintenance plan adjustment function 142 in the plan creation unit 140 adjusts the maintenance schedule by adjusting the regular maintenance schedule in the regular maintenance database 146 and the schedule of the above-described procurement plan and operation plan. Details of this adjustment procedure will be described later.
  • the information related to the maintenance plan composed of the operation plan and the procurement plan prepared by the plan creation function 141 and the regular maintenance plan adjustment function 142 is transmitted to the output management unit 160 by the plan creation unit 140.
  • the output management unit 160 stores and manages this in the maintenance plan database 161.
  • the life calculation function 151 in the performance prediction unit 150 is Referring to the work history for each component recorded in the component history database 153, the life history of the used component is calculated. In addition, the life calculation function 151 transmits information on the calculated life record to the output management unit 160. The output management unit 160 stores and manages this in the prediction result database 162. At this time, the downtime calculation function 152 of the performance prediction unit 150 identifies a part whose status has been exchanged in the part history database 153, that is, a part that has already reached the end of its life.
  • the downtime calculation function 152 is recorded from the load factor of each mining machine 10 (sensor data obtained from various sensors 11 attached to the mining machine 10) held in the part operation database 154. Calculate the downtime of the parts used. Details of this calculation method will be described later.
  • the downtime calculation function 152 determines whether there is a difference between the downtime based on the failure history thus obtained and the downtime based on the sensor data, and transmits the determination result to the client terminal 190 to present to the user.
  • the user may be allowed to select any downtime.
  • the downtime calculation function 152 may accept any downtime correction from the user via the client terminal 190.
  • the performance prediction unit 150 transmits the downtime uniquely determined in this way to the output management unit 160.
  • the output management unit 160 stores and manages this in the prediction result database 162.
  • the down time calculation function 152 may be executed in response to an instruction from the user not only when an abnormality occurs in the mining machine 10 but also during normal times.
  • the output management unit 160 included in the maintenance plan planning support system 100 also outputs the results output from the above-described abnormality diagnosis unit 120, countermeasure extraction unit 130, and plan creation unit 140 to the maintenance plan database 161 and the performance prediction unit 150.
  • the output results are held and managed in the prediction result database 162, respectively.
  • the data of the maintenance plan database 161 and the prediction result database 162 is output to the client terminal 190 accessed via the network 180. In this output, the content output to the client terminal 190 may be changed in response to a user request from the client terminal 190.
  • FIG. 2A shows an example of the phenomenon history database 123 in the present embodiment.
  • the phenomenon history database 123 is a database in which the history of phenomena observed for the mining machine 10 in the past is accumulated.
  • the data structure includes an occurrence date and time 202, a site ID 203, a machine ID 204, a model name 205, a phenomenon code 206, a phenomenon content 207, a part code 208, a part name 209, n, using the phenomenon ID 201 as a key.
  • This is a set of records composed of sensor data 210.
  • the phenomenon ID 201 an ID for uniquely identifying a phenomenon observed in the past for the mining machine 10 is stored.
  • the occurrence date 202 stores the date when the corresponding phenomenon occurred.
  • the site ID 203 stores the ID of the site where the mining machine 10 where the corresponding phenomenon is observed, that is, the mine.
  • the machine ID 204 stores the ID of the mining machine 10 in which the corresponding phenomenon is observed.
  • the model name 205 stores the model name of the mining machine 10.
  • the phenomenon code 206 stores a code indicating the corresponding phenomenon, and the phenomenon content corresponding to the code is stored in the phenomenon content 207.
  • the part code 208 stores a code indicating the part of the mining machine 10 where the phenomenon is observed, and the part name 209 stores the name of the part.
  • n sensor data 210 stores information observed by each sensor 11.
  • the information stored in the sensor data 210 may indicate the presence or absence of an abnormality, or may be a value itself observed by the sensor 11.
  • an abnormality detection parameter calculated by the monitoring system 170 from one or a plurality of sensor data may be stored.
  • FIG. 2B shows an example of the failure history database 124.
  • the failure history database 124 is a database in which a history of failures that have occurred in the past in the mining machine 10 is accumulated.
  • This failure history database 124 uses a failure ID 211 as a key, a phenomenon ID 212, an occurrence date and time 213, a machine ID 214, a model name 215, a part serial number 216, a part code 217, a part name 218, and a cause part number. 219, a cause component recycled product determination 220, an hour meter 221, a failure code 222, and a failure content 223.
  • the phenomenon ID 212 stores an ID for identifying a phenomenon observed in the mining machine 10 prior to the failure. This phenomenon ID 212 is common to the phenomenon ID 201 in the phenomenon history database 123 described above.
  • the occurrence date and time 213 stores the date and time when the corresponding failure occurred.
  • the machine ID 214 stores an ID for identifying the mining machine 10 in which the corresponding failure has occurred, and the model name 215 stores the model name of the corresponding mining machine 10.
  • the part serial number 216 stores a number for uniquely identifying a failed part in the mining machine 10.
  • the part code 217 and the part name 218 store a code indicating the part of the failed mining machine 10 and its name.
  • the cause part number 219 stores the product number of the failed part.
  • the cause component recycled product determination 220 stores a flag for determining whether the failed component is a recycled component or a new one.
  • the hour meter 221 stores an instruction value of an hour meter for operating time measurement provided in the mining machine 10. The indication value of this hour meter becomes the indication value at the time when the corresponding part fails.
  • the failure code 222 stores a code indicating the content of the failure that has occurred, and the failure content 223 stores the content of the failure that has occurred. Note that the relationship between each code and content in the failure history database 124 described above can also be managed by creating a separate master table in the same manner as the phenomenon and location in the phenomenon history database 123 described above.
  • FIG. 3 shows an example of the work history database 132 of the present embodiment.
  • the work history database 132 is a database that accumulates a history of maintenance work performed for failures that have occurred in the past in the mining machine 10.
  • the work history database 132 uses the work ID 301 as a key, a failure ID 302, a corresponding start date / time 303, a corresponding end date / time 304, a machine ID 305, a fault code 306, a fault content 307, a part code 308, and a part name. 309, cause part serial number 310, cause part number 311, work code 312, work content 313, replacement part serial number 314, replacement part number 315, and replacement part recycled product determination 316. It is an aggregate of. Of these, the failure ID 302 is common to the failure history database 124.
  • an ID for identifying the work performed for the failure corresponding to the failure ID 302 is stored.
  • the corresponding start date and time 303 and the corresponding end date and time 304 store the time when the corresponding work was started and the time when the corresponding work was completed.
  • the machine ID 305 stores an ID for identifying the mining machine 10 in which a failure has occurred and the maintenance work has been performed.
  • the failure code 306 and the failure content 307 store a code indicating the failure that has occurred and its content.
  • a code indicating the part where the failure has occurred and its name are stored.
  • the cause part serial number 310 stores a number that uniquely identifies the failed part, and the cause part number 311 stores the product number.
  • the work code 312 stores a code corresponding to the content of the maintenance work performed, and the work content 313 stores the content of the maintenance work. If the corresponding maintenance work involves parts replacement, the ID of the part to be newly attached to the mining machine 10 instead of the failed part is stored in the replacement part serial number 314.
  • the replacement part number 315 stores the product number of the replaced part, and the replacement part recycled product determination 316 stores a flag indicating whether or not the part is a recycled part.
  • each history database shown in FIG. 2A and FIG. 3 is the first database in the present invention, and is created and managed by a maintenance business operator, and is continued by performing the maintenance business. Expanded.
  • FIG. 4 shows an example of the maintenance work database 133 in this embodiment.
  • the maintenance work database 133 corresponds to the second database in the present invention that holds information on standard maintenance work defined for each work machine.
  • This maintenance work database 133 is a database that stores information on necessary resources and costs of standard maintenance work to be performed for each type of mining machine 10.
  • the maintenance work database 133 uses the model name 401 as a key, the work code 402, the work content 403, the part code 404, the part name 405, the part number 406, the replacement part number 407, the work cost 408, and the standard. This is a set of records composed of a work time 409, necessary equipment 410, and necessary maintenance staff skills 411.
  • the model name 401 described above stores the model name of the mining machine 10 to be subjected to maintenance work.
  • the work code 402 and the work content 403 respectively store a code for specifying the work content and the content thereof.
  • the part code 404, the part name 405, and the part number 406 respectively store a code indicating a target part where maintenance work is performed in the mining machine 10, a name thereof, and a product number of the target part. Even if the work code 402 has the same work content, the work code 402 is a different code when the cost, work time, and required resources differ depending on the type, part, and part of the mining machine 10.
  • the replacement part number 407 stores the product number of a part to be newly attached when the part is replaced in the corresponding maintenance work.
  • the replacement part number 407 is blank or a predetermined determination symbol is stored.
  • the work cost 408, the standard work time 409, the necessary equipment 410, and the necessary maintenance staff skill 411 include the cost required for the maintenance work, the time required for the maintenance work, the name of the equipment required for the maintenance work, and the maintenance. Stores the skill names of maintenance personnel required for the work.
  • FIG. 5 shows an example of the schedule database 143 in the present embodiment.
  • the schedule database 143 corresponds to a third database in the present invention that holds information on the availability of each person for maintenance work and each equipment.
  • the schedule database 143 is a collection of records including a date 500, a maintenance staff schedule 501, and an equipment schedule 502.
  • the schedule database 143 can be said to be a database that stores equipment owned by a maintenance company and a schedule of maintenance personnel employed.
  • the period in which the equipment and the maintenance staff can handle the maintenance work is represented by “1”, and the period in which the equipment and maintenance staff cannot handle is represented by “0”.
  • the date 500 is expressed in units of one day.
  • the maintenance manager or the like arbitrarily sets such as every hour, every eight hours, or every week. It's okay.
  • FIG. 6A shows an example of a parts inventory table 600 included in the parts procurement database 144 of this embodiment.
  • the parts procurement database 144 is composed of a parts inventory table 600, a transport means table 610, and a compatible parts table 620, and each stock and price of parts used for maintenance work or old version parts or recycled parts, and transportation.
  • the parts inventory table 600 is an aggregate of records including a part code 601, a part name 602, a part number 603, a recycled product determination 604, a warehouse 605, a stock 606, and a price 607. Each record shows how many parts are stocked in which warehouse and how much the price is.
  • the part code 601, the part name 602, the part number 603, and the recycled product determination 604 are information on the corresponding attribute of the inventory part.
  • the replacement parts used for the maintenance of the mining machine 10 are repaired and refurbished as new parts that are the latest version, old version parts that have not been retroactively implemented, and failed parts. There are recycled parts. Among these, the recycled parts are ranked according to the degree of wear.
  • N indicates a new article
  • Re-A”, “Re-B”, and “Re-C” indicate that a recycled part has a low level of wear, that is, a high-ranked one. Since the old version part is an old new article with only an old model number, it is “N” in the example of FIG. 6A. Of course, a notation for identifying the old version part may be used.
  • the warehouse 605 stores the name of the warehouse where the stock parts are stored, the stock 606 stores the number of stock parts, and the price 607 stores the unit price of the stock parts.
  • the stock 606 and the price 607 are variables that change with time, but here, it is assumed that the latest values are always stored.
  • FIG. 6B shows an example of the transportation means table 610 included in the parts procurement database 144 of this embodiment.
  • the transportation means table 610 included in the parts procurement database 144 is a collection of records including a warehouse 611, a transportation destination 612, a transportation means 613, a delivery date 614, and a transportation cost 615.
  • This record shows what kind of transportation is available from the warehouse as the transportation source to the site as the transportation destination, and how much time and cost it takes.
  • the warehouse 611 stores the name of the warehouse that is the transportation source of the parts.
  • a site name that is a transportation destination of the parts is stored in the transportation destination 612.
  • the transportation means 613 stores names of means used for parts transportation.
  • the delivery date and the cost in each case are stored in the delivery date 614 and the transportation cost 615, respectively.
  • FIG. 6C shows an example of the compatible component table 620 included in the component procurement database 144 of this embodiment.
  • the compatible parts table 620 included in the parts procurement database 144 is a collection of records including a model name 621, a part code 622, a part name 623, a part number 624, and a regular replacement interval 625.
  • This record shows a list of compatible parts that can be used in a certain part of a certain mining machine 10 and its regular replacement interval.
  • the model name 621 includes information indicating the model name of the target mining machine 10
  • the part code 622 includes information specifying the target part of the mining machine 10
  • the part name 623 includes the part code.
  • the names of the parts indicated by 622 are respectively stored.
  • the target mining machine 10 and its part can be specified by each information of the model name 621, the part code 622, and the part name 623.
  • the part number 624 stores a product number of a part that can be used by being attached to the target mining machine 10.
  • the parts that are specified by the information of the type name 621, the part code 622, and the part name 623 and that can be applied to a predetermined part of a certain mining machine 10 have the same specifications and functions, and are new parts with the latest model numbers. 3 parts of the old version parts and the recycled parts are included.
  • the periodic replacement interval 625 stores a component replacement interval recommended by a component manufacturer.
  • FIG. 7A shows an example of the operation loss table 700 included in the customer knowledge database 145 of this embodiment.
  • the customer knowledge database 145 stores the remaining life of each mining machine 10 according to the load factor at the time of failure and information on the economic loss in the user of the mining machine 10 due to the load factor reduction in association with each other. Corresponds to 5 databases.
  • the customer knowledge database 145 includes an operation loss table 700, a remaining life table 710, and a customer table 730.
  • the customer is a user of the mining machine 10 and a customer of the maintenance service of the mining machine 10.
  • the operation loss table 700 is a collection of records including a customer ID 701, a site ID 702, a model name 703, a part code 704, a part name 705, a load factor 706, and an operation loss 707.
  • the operation loss table 700 shows the customer's economic loss that occurs when the load factor of a certain part of the mining machine 10 is limited at a certain customer's site.
  • the customer ID 701 stores an ID for identifying a customer
  • the site ID 702 stores an ID for identifying a site where the corresponding mining machine 10 is operated.
  • the type name 703, the part code 704, and the part name 705, the type name of the mining machine 10, the code indicating the part, and the name of the part are stored.
  • the load factor 706 stores a load limiting rate when the load at the normal rated operation in the mining machine 10 is 100.
  • the “load” corresponds to the number of rotations and torque when the “part” is a motor, for example.
  • the operation loss 707 stores information on economic loss per unit time of a customer who operates the mining machine 10 that occurs when a load is limited. This operation loss 707 is characterized by the customer's operation policy, the type of resource mined at the site, and the part that limits the load.
  • FIG. 7B shows an example of the remaining life table 710 included in the customer knowledge database 145 of this embodiment.
  • the remaining life table 710 includes customer ID 711, site ID 712, model name 713, part code 714, part name 715, phenomenon code 716, phenomenon content 717, failure code 718, failure content 719, load This is a set of records composed of a rate 720 and a remaining life 721.
  • the customer ID 711 stores an ID for specifying a customer
  • the site ID stores an ID for specifying a site.
  • the model name 713, the part code 714, and the part name 715 store the model name of the target mining machine 10, the code indicating the part, and the name of the corresponding part.
  • the phenomenon code 716 and the phenomenon content 717 store a code indicating a phenomenon that has occurred in the corresponding part and the content of the corresponding phenomenon.
  • the failure code 718 and the failure content 719 store a code indicating the failure corresponding to the phenomenon and the content of the failure.
  • the load factor 720 stores the load factor at the corresponding part of the mining machine 10 that can be specified by the model name 713, the part code 714, and the part name 715.
  • the remaining life 721 stores a time from when an abnormality is detected at a corresponding part until a failure occurs.
  • the remaining life 721 is the phenomenon code at the site specified by the site code 714 and the site name 715 of the mining machine 10 of the model name 713 operated by the customer specified by the customer ID 711 at the site specified by the site ID 712. 716 and the phenomenon content 717, when the corresponding part is operated at the load factor of 720, it means how long the failure is stored in the failure code 718 and the failure content 719. .
  • FIG. 7C shows an example of the customer table 730 included in the customer knowledge database 145 of this embodiment.
  • the customer table 730 is an aggregate of records including a customer ID 731, a customer name 732, a site ID 733, a site name 734, a site type 735, and a country name code 736.
  • the customer ID 731 has an ID for identifying the customer
  • the customer name 732 has the name of the customer
  • the site ID 733 has an ID for identifying the site operated by the customer
  • the site name 734 has the ID of the site.
  • the name of the site type 735 stores the type of resource mined at the site
  • the country name code 736 stores a country name code representing the country in which the site exists.
  • the operation loss table 700, the remaining life table 710, and the customer table 730 described above are associated with each other using the customer ID and the site ID as keys.
  • FIG. 8 shows an example of the regular maintenance database 146 of this embodiment.
  • the regular maintenance database 146 corresponds to the ninth database of the present invention that stores a schedule of regular maintenance planned by the maintenance company for the mining machine 10.
  • the date on which the scheduled maintenance is planned is represented by “1”
  • the date on which the scheduled maintenance is not scheduled to be performed is represented by “0”.
  • the schedule unit may be arbitrarily set by the user.
  • FIG. 9 shows an example of the component history database 153 of this embodiment.
  • the parts history database 153 corresponds to a sixth database of the present invention that stores information on the attachment and removal of individual parts or their old version parts or recycled parts from the mining machine 10.
  • the component history database 153 includes a component serial number 901, a part code 902, a part name 903, a part number 904, a remanufactured product determination 905, a customer ID 906, a site ID 907, an attachment machine ID 908, and a status flag 909. And a collection of records composed of an attachment date 910 and a removal date 911.
  • the part serial number 901 stores a number that uniquely identifies the part that the maintenance company performs maintenance on.
  • the part code 902 and the part name 903 store a code indicating the part to which the corresponding part is attached and its name.
  • the part number 904 and the recycled product determination 905 store the product number of the component specified by the component serial number 901 and the recycled product determination flag, respectively.
  • Parts are put on the market as recycled parts through a cycle of mounting ⁇ removal ⁇ recycling ⁇ mounting. In this process, the recycled parts are ranked according to the degree of wear as described above. Recycled parts tend to lower the rank of the recycled product determination 905 as the reproduction is repeated.
  • the life from the attachment to the removal of the parts is called the life.
  • the lifespan is managed by assigning different parts serial numbers 901 in order to distinguish the parts of the same individual for each reproduction opportunity.
  • the mounting machine ID 909 stores the ID of the mining machine 10 to which the corresponding part is attached.
  • the customer ID 907 and the site ID 908 respectively store the ID of the customer who operates the mining machine 10 and the ID of the site where the mining machine 10 operates.
  • the status flag 910 stores the current status of the part.
  • the installation date / time 911 and the removal date / time 912 store the date / time when the corresponding part was attached to the mining machine 10 and the date / time when the part was removed from the mining machine 10.
  • FIG. 10A shows an example of an operation determination table 1000 included in the component operation database 154 of this embodiment.
  • the part operation database 154 stores information for determining whether to operate / stop a part using a value acquired from the sensor 11 attached to the part.
  • a part attached to a certain part of the mining machine 10 is stored in the part operation database 154.
  • An operation determination table 1000 that stores a determination formula for determining whether or not it is operating, and an operation result table 1010 that stores sensor data related to the mining machine 10 during a period specified by the user.
  • the operation determination table 1000 shown in FIG. 10A includes information of a model name 1001, a part code 1002, a part name 1003, a determination item 1004, a determination value 1005, and a determination condition 1006.
  • the determination item 1004 is a value such as the rotation speed or torque.
  • a condition in which “period” of the determination item 1004 is “2000” or more as the determination value 1005 is a condition for determining “operation”.
  • FIG. 10B shows an example of an operation result table 1010 included in the component operation database 154 of the present embodiment.
  • the operation result table 1010 is a collection of records including a component serial number 1011, an item 1012, a period 1013, and an average value 1014.
  • the part serial number 1011 stores a part number that uniquely identifies the part that the sensor 11 is measuring.
  • the item 1012 stores the type of sensor data stored for the corresponding part.
  • a period 1013 information on a period during which the sensor 11 has measured the corresponding part indicated by the part serial number 1011 is stored.
  • the average value 1014 stores an average value of the sensor data measured by the sensor 11 for the corresponding component indicated by the component serial number 1011 in the period 1013.
  • the period 1013 is set to one hour as a unit time, but may be appropriately changed by a user operation.
  • FIG. 11A shows an example of the abnormality diagnosis result table 1100 included in the maintenance plan database 161 of this embodiment.
  • the maintenance plan database 161 includes an abnormality diagnosis result table 1100, a countermeasure extraction result table 1120, an execution candidate table 1130, and a procurement / operation plan table 1140. These tables 1100 to 1140 are created as outputs of the abnormality diagnosis unit 120, the countermeasure extraction unit 130, the plan creation unit 140, and the performance prediction unit 150.
  • the abnormality diagnosis result table 1100 is a table output by the abnormality diagnosis unit 120, and includes an abnormality ID 1101, an occurrence date and time 1102, an hour meter 1103, a customer ID 1104, a site ID 1105, a machine ID 1106, a part serial number 1107, and a phenomenon.
  • This is a set of records including a code 1108, a phenomenon content 1109, a failure code 1110, and a failure content 1111.
  • the anomaly ID 1101 is an anomaly identified by the anomaly diagnosis unit 120 described above, and stores an ID that uniquely identifies an anomaly that is considered to be a sign of failure.
  • the occurrence date and time 1102 and the hour meter 1103 store the date and time and hour value when the corresponding abnormality is detected.
  • the customer ID 1104, the site ID 1105, the machine ID 1106, and the part serial number 1107 are respectively an ID that identifies a customer who owns the mining machine 10 that has detected an abnormality, and an ID that identifies a site where the mining machine 10 is operating.
  • the ID for identifying the mining machine 10 and the ID for identifying the component in which the abnormality is detected are stored.
  • the result of diagnosis by the phenomenon diagnosis function 121 described above is stored in the phenomenon code 1108 and the phenomenon content 1109
  • the result of diagnosis by the failure diagnosis function 122 is stored in the failure code 1110 and the failure content 1111.
  • FIG. 11B shows an example of the measure extraction result table 1120 included in the maintenance plan database 161 of this embodiment.
  • the countermeasure extraction result table 1120 includes a countermeasure ID 1121, an abnormality ID 1122, a work code 1123, a work content 1124, a standard work time 1125, a work cost 1126, and necessary equipment 1127 output from the above-described countermeasure extraction unit 130. , A set of records composed of necessary maintenance personnel skills 1128.
  • the countermeasure ID 1121 stores an ID that uniquely identifies a countermeasure, that is, maintenance work planned for the abnormality corresponding to the abnormality ID 1101 in the abnormality diagnosis result table 1100 described above.
  • the abnormality ID 1122 stores an abnormality ID 1122 that is common to the abnormality ID 1101 in the abnormality diagnosis result table 1100 described above.
  • the work code 1123 and the work content 1124 store the maintenance work code extracted from the work history database 132 and its content.
  • the standard work time 1125, work cost 1126, necessary equipment 1127, and necessary maintenance staff skill 1128 include the time, cost, and equipment required for the maintenance work extracted from the maintenance work database 133 for the relevant maintenance work. And maintenance staff skill information are stored.
  • FIG. 11C shows an example of the implementation candidate table 1130 included in the maintenance plan database 161 of this embodiment.
  • the implementation candidate table 1130 is a table that the plan function 141 of the plan creation unit 140 extracts and outputs from the measure extraction result table 1120 and the schedule database 143 that are the outputs of the measure extraction unit 130.
  • This execution candidate table 1130 is an aggregate of records including schedule ID 1131, countermeasure ID 1132, execution candidate 1133, possible date 1134, corresponding maintenance staff 1135, corresponding equipment 1136, and tx1137.
  • the schedule ID 1131 stores an ID for identifying a candidate period for performing maintenance work.
  • the countermeasure ID 1132 stores an ID that identifies the countermeasure to be implemented, in common with the countermeasure ID 1121 in the countermeasure extraction result table 1120 described above.
  • the execution candidate 1133 stores a value that matches the schedule ID 1131.
  • the execution date 1134 stores a schedule that allows the maintenance work to be extracted, which is extracted by the plan creation function 141 described above.
  • the maintenance staff 1135 and the equipment 1136 store the maintenance staff name and the equipment name corresponding to the correspondence, respectively.
  • a grace time from the current time to the execution candidate date of maintenance work is stored.
  • FIG. 11D shows an example of the procurement / operation table 1140 included in the maintenance plan database 161 of this embodiment.
  • the procurement / operation table 1140 stores the results output by the plan creation function 141 of the plan creation unit 140 described above with reference to the inventory parts database 145 and the customer knowledge database 145.
  • a procurement / operation table 1140 shown in FIG. 11D is a table configuration example showing the contents planned for maintenance work including parts replacement work.
  • the procurement / operation table 1140 includes a plan ID 1141, a schedule ID 1142, a part number 1143, a recycled product determination 1144, a warehouse 1145, a transportation means 1146, a delivery date 1147, a part price 1148, a transportation cost 1149, a load
  • plan ID 1141 stores an ID for uniquely identifying the procurement / operation plan. Note that if the maintenance work does not include parts replacement and therefore does not require parts procurement, the plan ID 1141 is an ID for specifying the operation plan.
  • the schedule ID 1142 stores an ID corresponding to the schedule ID 1131 of the execution candidate table 1130 described above.
  • the part number 1143 and the recycled product determination 1144 store the component number of the component to be newly installed in the replacement operation of the component and the recycled product determination flag of the corresponding component.
  • these part number 1143 and remanufactured product judgment 1144 are blank or some judgment symbols are stored.
  • the warehouse 1145, the transportation means 1146, the delivery date 1147, the part price 1148, and the transportation cost 1149 respectively include the name of the warehouse for procuring replacement parts, the transportation means, the delivery date, the price of the corresponding part, and the transportation cost. Stored.
  • the warehouse 1145, the transportation means 1146, the delivery date 1147, the part price 1148, and the transportation cost 1149 are blank or Some kind of judgment symbol is stored.
  • the load factor 1150 stores the upper limit of the load factor of the corresponding part (part replacement target part) in the mining machine 10 when the maintenance plan is executed.
  • the operation loss 1151 stores the customer's economic loss caused by limiting the load on the mining machine 10 to the value indicated by the load factor 1150.
  • the work loss 1152 stores the economic loss of the customer due to the stoppage of the operation of the mining machine 10 when the maintenance work is performed.
  • the procurement / operation table 1140 may be managed separately as a procurement table for parts procurement and an operation table for operations such as load factor and operation loss.
  • FIG. 12 shows an example of the prediction result database 162 in this embodiment.
  • the prediction result database 162 includes an object 1201, a part code 1202, a part name 1203, a part number 1204, a recycled product determination 1205, a regular replacement interval 1206, an average life 1207, and a history-based average DT (downtime). 1208, an operation base average DT 1209, and a set of records composed of 1210 samples.
  • This record is the output of the life calculation function 151 and the downtime calculation function 152 of the performance prediction unit 150.
  • the target 1201 specifies the range of the mining machine 10 for which the lifetime calculation function 151 and the downtime calculation function 152 described above are targets of performance prediction, and the country code and customer ID indicating the corresponding mining machine 10 and the like. Specified by the site ID.
  • the value of the site ID is set as the target 1201.
  • the part code 1202 and the part name 1203 store a code indicating the part targeted for performance prediction and its name.
  • the part number 1204 and the recycled product determination 1205 store the product number of the component subjected to performance prediction and the recycled product determination flag.
  • the periodic replacement interval 1206 stores the periodic replacement interval of the part whose performance is to be predicted, that is, the part indicated by the part number 1204.
  • the value of the regular replacement interval 1206 is, for example, a design replacement interval value defined by a component manufacturer.
  • the average life 1207 stores a value of the average life calculated by the above-described life calculation function 151 based on the component history database 153. The value of the average life 1207 is to be compared with the value of the regular replacement interval 1206 described above.
  • the history-based average DT 1208 stores the average downtime value calculated by the above-described downtime calculation function 152 based on the component history database 153 and the failure history database 124.
  • the operation base average DT 1209 stores the average downtime value calculated by the downtime calculation function 152 described above based on the component history database 153 and the component operation database 154.
  • the number of samples 1210 stores the number of parts processed by the life calculation function 151 and the downtime calculation function 152.
  • FIG. 13 is a flowchart showing a processing procedure example 1 of the maintenance plan planning method in the present embodiment. This flow is executed when the maintenance planning support system 100 receives a notification from the monitoring system 170 that the value of the sensor data of the sensor 11 exceeds a predetermined threshold.
  • the process S1301 is executed by the phenomenon diagnosis function 121 of the abnormality diagnosis unit 120.
  • the phenomenon diagnosis function 121 identifies and responds to a phenomenon detected by the monitoring system 170 by comparing the sensor data abnormality detected by the monitoring system 170 with the sensor data 210 of the phenomenon history database 123. Information on the corresponding phenomenon such as the phenomenon ID 201, the phenomenon code 206, and the phenomenon content 207 is extracted from the phenomenon history database 123.
  • the phenomenon diagnosis function 121 includes information related to the phenomenon identified by itself, information related to a part for which the monitoring system 170 detects an abnormality in sensor data (eg, customer ID, site ID, machine ID, part serial number, etc.) Is output to the abnormality diagnosis result table 1100 in the maintenance plan database 161.
  • information related to a part for which the monitoring system 170 detects an abnormality in sensor data eg, customer ID, site ID, machine ID, part serial number, etc.
  • the failure diagnosis function 122 refers to the failure history database 124 using the value of the phenomenon ID 201 extracted in the above-described process S1301 as a key, identifies a failure corresponding to the phenomenon detected by the monitoring system 170, and sets the failure ID 211 and the failure code. 222 is extracted.
  • the failure diagnosis function 122 outputs information on the failure thus extracted to the abnormality diagnosis result table 1100 in the maintenance plan database 161.
  • step S1303 is executed by the measure extracting function 131 of the measure extracting unit 130.
  • the countermeasure extracting function 131 refers to the work history database 132 by using the value of the failure ID 211 extracted by the failure diagnosis function 122 in the process S1302 described above as a key, and performs maintenance work that has been performed for the same failure in the past. Then, the work code 312 is extracted. The measure extraction function 131 outputs the extracted result to the measure extraction result table 1120.
  • step S1304 is executed by the measure extraction function 131 of the measure extraction unit 130.
  • the countermeasure extracting function 131 refers to the maintenance work database 133 using the work code 312 extracted in the processing S1303 as a key, and the work cost 408, the standard work time 409, the necessary equipment 410, the maintenance staff skill required for the corresponding maintenance work. The value of 411 is extracted.
  • the measure extraction function 131 outputs the result extracted here to the measure extraction result table 1120.
  • step S1305 is executed by the plan creation function 141 of the plan creation unit 140.
  • the plan creation function 141 reads, from the schedule database 143, the schedule of the maintenance staff who possesses the equipment corresponding to the necessary equipment 410 extracted in the above-described processing S1304 and the skills indicated by the maintenance staff skills 411. From the schedule read here, the plan creation function 141 identifies a “vacant day” that can be handled by the relevant equipment and maintenance personnel, that is, no other work schedules, as the implementation candidate date, and implements from the present time. The period tx until the candidate date is calculated. The plan creation function 141 outputs information on the identified implementation candidate date (corresponding to “execution date” in the implementation candidate table 1130) and the period tx to the implementation candidate table 1130.
  • step S1306 is executed by the plan creation function 141.
  • the plan creation function 141 determines whether the maintenance work specified by the measure extraction function 131 in the above-described process S1303 includes a parts replacement work. When the maintenance work does not include the part replacement work (S1306: No), the plan creation function 141 executes the process S1307. On the other hand, when the maintenance work includes parts replacement work (S1306: Yes), the plan creation function 141 executes the process S1308.
  • plan creation function 141 creates an operation plan with reference to the implementation candidate table 1130 and the customer knowledge database 145, and outputs the operation plan to the procurement / operation table 1140.
  • the detailed procedure for creating the operation plan will be described later with reference to FIG.
  • the process S1308 is executed by the plan creation function 141 of the plan creation unit 140.
  • the plan creation function 141 creates a procurement / operation plan by referring to the execution candidate table 1130, the parts procurement database 144, and the customer knowledge database 145, and outputs the procurement / operation plan to the procurement / operation plan table 1140. Details of the procurement / operation plan creation will be described later with reference to FIG.
  • step S1309 is executed by the performance prediction unit 150.
  • the life calculation function 151 of the performance prediction unit 150 calculates the average life of the corresponding part based on the part history database 153 for the replacement part indicated in the procurement / operation plan created in the above-described process S1308.
  • the downtime calculation function 152 of the performance prediction unit 150 calculates the downtime of the corresponding part based on the part history database 153, the failure history database 124, and the part operation database 154.
  • the average lifetime value calculated by the lifetime calculation function 151 and the downtime value calculated by the downtime calculation function 152 are output to the prediction result database 162 by each function.
  • Each process in the life calculation function 151 and the downtime calculation function 152 will be described later with reference to FIGS.
  • step S1310 is executed by the regular maintenance plan adjustment function 142 of the plan creation unit 140.
  • the regular maintenance plan adjustment function 142 refers to the operation / procurement plan table 1140 created by the above-described plan creation function 141 and the regular maintenance database 146, and performs the implementation candidate date and the scheduled maintenance execution schedule indicated by the operation / procurement plan. It is determined whether or not the date matches. If the execution candidate date matches the scheduled scheduled maintenance date (S1310: Yes), the scheduled maintenance plan adjustment function 142 executes step S1311. On the other hand, when the execution candidate date does not match the scheduled scheduled maintenance date (S1310: No), the scheduled maintenance plan adjustment function 142 executes step S1312.
  • the process S1311 is executed by the plan creation function 141 of the plan creation unit 140.
  • the plan creation function 141 sets the scheduled maintenance execution date as the only execution candidate date, and executes the processing S1307 to create an operation plan when the above-described operation / procurement plan does not include parts replacement work.
  • the plan creation function 141 creates a procurement / operation plan by executing step S1308 when the above-described operation / procurement plan includes parts replacement work.
  • the plan creation function 141 outputs the operation / procurement plan and operation plan thus created to the procurement / operation plan table 1140.
  • the output management unit 160 outputs the maintenance plan and the prediction result stored in the maintenance plan database 161 and the prediction result database 162 of the output management unit 160 in the procedure so far to the client terminal 190.
  • the client terminal 190 outputs the maintenance plan and the prediction result on the display, and is used for evaluation and examination of the maintenance plan by the corresponding user.
  • FIG. 14 is an example of an operation plan creation procedure (processing S1307) in the present embodiment.
  • the process S1307 is executed by the plan creation function 141 of the plan creation unit 140.
  • the plan creation function 141 can refer to the abnormality diagnosis result table 1100, the countermeasure extraction result table 1120, and the implementation candidate table 1130 of the maintenance plan database 161 output in the processing S1301 to S1305 described above. To do.
  • the plan creation function 141 extracts, for example, the one related to the implementation candidate with the earliest implementation date 1134 among the records in the implementation candidate table 1130 (execution candidate x). From time to date 1134 is acquired and stored in the memory 113.
  • the plan creation function 141 specifies the abnormality ID 1122 in the countermeasure extraction result table 1120 using the value of the countermeasure ID 1132 indicated by the record extracted above as a key, and uses the abnormality ID 1122 as a key to diagnose the abnormality diagnosis result table 1100. Then, information such as a customer ID, a site ID, a phenomenon code, a phenomenon content, a failure code, and a failure content relating to a part to be maintained is specified. Further, the plan creation function 141 collates the information specified here with the remaining life table 710 of the customer knowledge database 145, and acquires the value of the remaining life 721 of the corresponding part. The plan creation function 141 compares the remaining life value acquired in this way with the time tx from the current time to the execution date 1134, and determines whether or not a failure occurs before the maintenance work is performed.
  • the plan creation function 141 estimates that the corresponding part will fail by the execution date 1134 of the execution candidate x, and the next process S1403 is executed.
  • FIG. 19A shows the relationship between the remaining life and the time tx from the present time to the implementation date 1134.
  • the execution candidate 1 has a relationship of t1 ⁇ remaining life, and it is predicted that no component failure will occur by the execution date 1134 of the maintenance work.
  • the execution candidate 2 has a relationship of t2> remaining life, and it is expected that a component will fail by the execution date 1134 of the maintenance work.
  • the plan creation function 141 refers to the remaining life table 710 of the customer knowledge database 145, and determines the load factor at which the corresponding part does not fail by the date of the maintenance work, that is, the maximum load factor that satisfies the remaining life> tx. Identify. For example, when the remaining life is 2 days shorter than tx, the remaining life table 710 may specify the load factor “90” that is 3 days longer than the case where the load factor is “100”.
  • step S1405 the plan creation function 141 calculates an operation loss and a work loss.
  • the mining machine 10 is not fully operated during that period, and the customer's profit is reduced, resulting in economic loss. Will result.
  • this economic loss be an operational loss.
  • the plan creation function 141 collates the load factor acquired in step S1402 or step S1403 and step S1404 with the operation loss table 700 of the customer knowledge database 145, and specifies the value of the corresponding operation loss 707.
  • the plan creation function 141 calculates the operation loss by multiplying the value of the specified operation loss 707 by the above-described value of tx (the grace time from the current time to the maintenance work implementation date 1134).
  • the plan creation function 141 multiplies the standard work time 1125 extracted from the measure extraction result table 1120 by the value of the operation loss 707 in the case of the load factor “0” in the operation loss table 700 of the customer knowledge database 145 to calculate the work loss. calculate.
  • the plan creation function 141 outputs the load factor, operation loss, and work loss corresponding to the execution candidate x to the procurement / operation plan table 1140 of the maintenance plan database 161.
  • step S1307 maintenance work that does not include parts replacement work is targeted.
  • the plan creation function 141 uses the part number 1143, the recycled product determination 1144, the warehouse 1145, and the like in the procurement / operation plan table 1140.
  • the values of the transportation means 1146, the delivery date 1147, the part price 1148, and the transportation cost 1149 are blank, or some judgment symbol is output.
  • the plan creation function 141 refers to the execution candidate table 1130 of the maintenance plan database 161, and determines whether there are other execution candidates that have not yet been processed in the above-described processes S1401 to S1406. . If there are other execution candidates (S1407: Yes), the plan creation function 141 executes the process S1401 again. On the other hand, when there is no other implementation candidate (S1407: No), the plan creation function 141 ends the flow, that is, the process S1307.
  • FIG. 15 is an example of a procurement / operation plan creation procedure (processing S1308) in the present embodiment.
  • the process S1308 is executed by the plan creation function 141 of the plan creation unit 140.
  • the plan creation function 141 can refer to the abnormality diagnosis result table 1100, the countermeasure extraction result table 1120, and the execution candidate table 1130 of the maintenance plan database 161 output in the processing S1301 to S1305 described above.
  • the plan creation function 141 extracts, for example, the implementation candidate whose implementation date 1134 is the latest from the implementation candidate table 1130 (execution candidate x), and the time tx from the current time to the implementation date 1134. Are extracted from the corresponding record and stored in the memory 113.
  • the plan creation function 141 extracts the value of the part serial number 1107, which is the ID of the part in which the abnormality is detected, from the phenomenon diagnosis result table 1100 of the maintenance plan database 161.
  • the plan creation function 141 uses the value of the part serial number 1107 extracted here as a key to refer to the part history database 153 and extracts the part number and each value of the recycled product determination. Further, the plan creation function 141 reads the parts inventory table 600 of the parts procurement database 144 using the extracted part number and each value of the remanufactured product as a key, acquires the value of the inventory 606 of the corresponding part, and stores it in the memory 113. Store.
  • the plan creation function 141 selects a warehouse 605 having one or more stocks from the value of the stock 606 in the parts procurement database 144 read in S1502.
  • the plan creation function 141 specifies the corresponding record in the abnormality diagnosis result table 1100 using the abnormality ID of the execution candidate x described above as a key, and the mining machine 10 in which the abnormality is detected from this record.
  • the value of the site ID 1105 of the site where is operating is extracted.
  • the plan creation function 141 collates the value of the site ID 1105 with the customer table 730 in the customer knowledge database 145 and specifies the value of the site name 734 such as “siteA”.
  • site name the transportation means 613 in which the value of the warehouse 611 in the transportation means table 610 is the value of the warehouse 605 described above is selected.
  • the plan creation function 141 maintains the values of the parts extracted in process S1502, the transport means selected in process S1504, the transport costs corresponding to the transport means, and the delivery date values. Stored in the corresponding column of the procurement / operation plan table 1140 of the plan database 161.
  • step S1506 the plan creation function 141 determines whether or not replacement parts used for the maintenance work are delivered from the warehouse by the maintenance work execution date 1134.
  • the plan creation function 141 compares the value of the delivery date 1147 stored in the procurement / operation plan table 1140 with the above-described time tx from the current time to the implementation date 1134, and if delivery date> tx (S1506: Yes), it is estimated that the corresponding part is scheduled to be delivered by the maintenance work implementation date 1134, and the process S1507 is executed.
  • the plan creation function 141 estimates that the relevant part will not be delivered by the maintenance work implementation date 1134, and executes the process S1515.
  • the plan creation function 141 compares the remaining life with tx in the same manner as in process S1402 described above, and determines whether or not a failure occurs in the part to be replaced by the maintenance work implementation date 1134. judge.
  • the plan creation function 141 determines that there is no problem until the date of maintenance work even if the corresponding part is fully operated as usual, and the procurement / operation plan table 1140
  • the load factor of the corresponding part is set to “100”.
  • the plan creation function 141 refers to the remaining life table 710 of the customer knowledge database 145 in the same manner as in the above-described process S1403.
  • the maximum load factor that satisfies> tx is specified. For example, when the remaining life is 2 days shorter than tx, the remaining life table 710 may specify the load factor “90” that is 3 days longer than the case where the load factor is “100”.
  • step S1510 the plan creation function 141 calculates operation loss and work loss in the same manner as in step S1405 described above.
  • the plan creation function 141 collates the load factor acquired in step S1507, step S1508, or step S1509 with the operation loss table 700 of the customer knowledge database 145, and specifies the value of the corresponding operation loss 707.
  • the plan creation function 141 calculates the operation loss by multiplying the value of the specified operation loss 707 by the above-described value of tx (the grace time from the current time to the maintenance work implementation date 1134). Moreover, since the mining machine 10 is stopped during the maintenance work, the load factor of the corresponding part during the maintenance work becomes “0”. In that case, during that period, the mining machine 10 is stopped, and the profit of the customer is reduced, resulting in an economic loss. This economic loss is defined as work loss.
  • the plan creation function 141 multiplies the standard work time 1125 extracted from the measure extraction result table 1120 by the value of the operation loss 707 in the case of the load factor “0” in the operation loss table 700 of the customer knowledge database 145 to calculate the work loss. calculate.
  • the plan creation function 141 outputs the procurement / operation plan to the procurement / operation table 1140 of the maintenance plan database 161 in the same manner as the above-described process S1406.
  • the plan creation function 141 includes a part number 1143 in the procurement / operation plan table 1140 and a remanufactured product determination 1144.
  • the value of the part serial number 1107 of the corresponding part extracted in the above-described processing S1502 and a remanufactured product determination flag. Is stored respectively.
  • the plan creation function 141 stores the value of the warehouse 605 selected in the above-described processing S1503 in the warehouse 1145 in the procurement / operation plan table 1140.
  • plan creation function 141 stores the transportation means selected in the above-described processing S1504 in the transportation means 1146 in the procurement / operation plan table 1140, and similarly, the delivery date 1147, the part price 1148, and the transportation cost 1149 are stored. Stores the delivery date, price, and transportation cost stored in step S1505.
  • the plan creation function 141 refers to the part inventory table 600 of the part procurement database 144 by the same method as in the above-described process S1503, and determines whether the inventory of the target part is in another warehouse.
  • the plan creation function 141 executes the process S1503.
  • the plan creation function 141 executes the process S1514.
  • the plan creation function 141 refers to the compatible part table 620 of the part procurement database 144 and determines whether there is a compatible part of the part whose inventory has been confirmed in the above-described process S1502.
  • the plan creation function 141 uses values such as the site ID 1105, machine ID 1106, and fault code 1110 (extracted from the abnormality diagnosis result table 1100) related to the parts whose inventory has been confirmed in step S1502 as keys.
  • the record is searched in the failure history database 124, and the model name 215, the part code 217, and the part name 218 of the mining machine 10 are specified from the corresponding record.
  • the plan creation function 141 executes a search in the compatible part table 620 using the values of the type name 215, the part code 217, and the part name 218 specified here as keys, and the type name 621, the part code 622, and the part name 623 are searched. Although each value is common, it is determined whether there is a part that is different from the part number of the part whose inventory has been confirmed in the above-described processing S1502, that is, a compatible part. In the process S1514, when it is determined that there is a compatible part (S1514: Yes), the plan creation function 141 executes the process S1502 again. On the other hand, when it is determined that there is no compatible part (S1514: No), the plan creation function 141 executes the process S1515.
  • step S1515 the plan creation function 141 refers to the execution candidate table 1130 of the maintenance plan database 161 as in the above-described step S1406, and determines whether there are other unprocessed execution candidates. If there is another unprocessed execution candidate in this determination (S1515: Yes), the plan creation function 141 executes the process S1501 again. On the other hand, when there is no other unprocessed execution candidate (S1515: No), the plan creation function 141 ends this flow, that is, the process S1308.
  • FIG. 16 to FIG. 18 show an example of a life / downtime simulation execution procedure (processing S1309) in the present embodiment.
  • the life / downtime simulation process includes a life simulation S1309 (a) shown in FIG. 16, a downtime simulation S1309 (b) shown in FIG. 17, and a downtime simulation S1309 (c) shown in FIG. These are independent of each other and may be executed from any processing.
  • the life prediction simulation shown in FIG. 16 is executed by the life calculation function 151 of the performance prediction unit 150, and the downtime simulation shown in FIGS. 17 and 18 is executed by the downtime calculation function 152.
  • the life calculation function 151 and the downtime calculation function 152 can refer to the maintenance plan database 161 output from the above-described processing S1301 to processing S1308.
  • FIG. 16 is an example of a procedure for performing a life simulation in the present embodiment.
  • the life calculation function 151 refers to the part history database 153 and calculates the life of parts used and replaced in the past.
  • the life calculation function 151 reads the component history database 153.
  • the life calculation function 151 extracts the part number 1143 of the replacement part used for the maintenance work and each value of the recycled product determination 1144 from the procurement / operation plan table 1140 of the maintenance plan database 161, Using these values as keys, the parts history database 153 is searched, and records relating to the corresponding parts in which the values of the part number 1143 and the recycled product determination 1144 and the values of the part number 9043 and the recycled product determination 905 match are specified. Further, the life calculation function 151 reads the value of the status flag 910 from each record extracted here, specifies the record of the component whose value is “replaced”, and stores the data in the memory 113, for example. To do.
  • the life calculation function 151 calculates the difference between the values of the attachment date / time 911 and the removal date / time 912 in the data of each part obtained in the above-described process S1602, as the service life of the corresponding part.
  • the lifetime calculation function 151 executes the determination in step S1604 to determine whether the lifetime calculation process, that is, the process S1603 has been executed for all the data extracted in the process S1602, and thereby to all the data extracted in the process S1602. The above-described life calculation is executed.
  • the life calculation function 151 calculates an average value of the life values calculated for each data in process S1603, and outputs this as the value of the average life 1208 in the prediction result database 162. Further, the life calculation function 151 outputs the number of data specified in the process S1603 to the prediction result database 162 as the number of samples 1211.
  • the life calculation function 151 uses the part number 1143 of the replacement part used for maintenance work extracted in the above-described process S1602 and the part specified by the set of each value of the recycled product determination 1144. It is determined whether or not a plan using other different parts is stored in the procurement / operation table 1140 of the maintenance plan database 161. When there is a plan using another part (S1606: Yes), the life calculation function 151 executes the process from step S1602 again with the part as a processing target. On the other hand, when there is no plan using other parts (S1606: No), the life calculation function 151 ends this flow, that is, the process S1309 (a).
  • FIG. 17 is an example of a procedure for performing a downtime simulation based on the failure history database 124 and the component history database 153 in the present embodiment.
  • the downtime calculation function 152 refers to the failure history database 124 and the component history database 153 to calculate the downtime of the parts that have been used and replaced in the past.
  • the downtime calculation function 152 reads the parts history database 153 and performs maintenance work from the procurement / operation plan table 1140 of the maintenance plan database 161 in the same manner as the above-described processes S1601 and S1602.
  • the part number 1143 of the replacement part to be used and each value of the recycled product judgment 1144 are extracted, and the parts history database 153 is searched using these values as keys, and the part number 1143 and each value of the recycled product judgment 1144, A record relating to the corresponding part in which the values of the part number 9043 and the recycled product determination 905 match is specified.
  • the downtime calculation function 152 reads the value of the status flag 910 from each record extracted here, identifies the record of the part whose value is “replaced”, and stores the data in the memory 113, for example. Store.
  • step S1703 the downtime calculation function 152 acquires a part serial number from the data obtained in step S1702.
  • step S1704 the downtime calculation function 152 refers to the failure history database 124 using the acquired component serial number as a key.
  • the downtime calculation function 152 referring to the failure history database 124 determines whether or not the component indicated by the above-described component serial number has a failure history in the process S1705. When there is a failure history in the corresponding part (s1705: Yes), the downtime calculation function 152 executes the process S1706. On the other hand, when there is no failure history in the corresponding part (s1705: No), the downtime calculation function 152 executes step S1708.
  • step S1706 the downtime calculation function 152 refers to the work history database 132 using the failure ID 211 in the failure history database 124 as a key, and sets the difference between the corresponding start date 303 and the corresponding end date 304 of the corresponding task as the failure ID 211. Calculate as the corresponding downtime.
  • step S1707 the downtime calculation function 152 determines whether or not the downtime has been calculated for all the parts determined to have a failure in the above-described step S1705. When there is a failure whose downtime is not determined (S1707: No), the downtime calculation function 152 repeatedly executes the process S1706, and when the downtime is calculated for all failures (S1707: Yes), the process S1707 is performed. Execute.
  • step S1708 the downtime calculation function 152 determines whether the last line of the data extracted from the part history database 153 in step S1702 has been reached. If the last line has been reached (S1708: Yes), the downtime calculation function 152 executes step S1709. On the other hand, when there is still data (S1708: No), the downtime calculation function 152 executes the processing after the processing S1703 again to calculate the downtime of the corresponding part.
  • the downtime calculation function 152 calculates the average value of the downtime calculated in the above-described process S1706, and stores this as the value of the history base average DT1209 of the performance prediction database 162.
  • step S1710 the downtime calculation function 152 determines whether there is a plan using other parts in the procurement / operation table 1140 of the maintenance plan database 161 in the same manner as in step S1606.
  • the downtime calculation function 152 executes the process after the above-described process S1702 again with the part as a processing target part.
  • the downtime calculation function 152 ends this flow, that is, the process S1309 (b).
  • FIG. 18 is an example of a downtime simulation execution procedure in the present embodiment.
  • the downtime calculation function 152 refers to the failure history database 124, the component history database 153, and the component operation database 154, and calculates the downtime of the parts that have been used and replaced in the past.
  • the basic procedure of the implementation procedure (S1309 (c)) shown here is the same as the above-described processing S1309 (b). Therefore, here, the procedure from the processing S1804 to the processing S1807 for calculating the downtime and the processing S1809 for the output, which are different procedures from the processing S1309 (b), will be described.
  • step S1804 the downtime calculation function 152 narrows down the data in the component operation database 154 using the component serial numbers acquired in the same manner as in steps S1701 to S1703.
  • the downtime calculation function 152 refers to the value of the period 1013 in the operation result table 1010 of the component operation database 154 captured as described above, and calculates a determination unit time (example in FIG. 10B). (The unit time is 1 hour).
  • the downtime calculation function 152 refers to the determination value 1005 and the determination condition 1006 in the operation determination table 1000 of the component operation database 154, and the average value 1014 of the operation result table 1010 is smaller than the determination value 1005.
  • the record is identified as having the corresponding part “stopped”. Further, the downtime calculation function 152 adds the above unit time values for the number of records specified as “stopped”, and adds up the time determined to be “stopped”.
  • the downtime calculation function 152 displays the removal date and time 912 of the part history database 153 and the period 1013 of the corresponding record (record of the operation result table 1010) that is the target of the integration process in the process S1805 described above.
  • the date indicated by this period 1013 reaches the date indicated by the removal date and time 912 (S1806: Yes)
  • the integration process of the above-described process S1805 is terminated, and the process S1807 is executed.
  • the downtime calculation function 152 returns the process to the above-described process S1805.
  • step S1807 the downtime calculation function 152 stores the time accumulated so far, that is, the downtime, in the memory 113 as the operation base downtime of the corresponding part. Further, the downtime calculation function 152 repeats the process up to the process S1807 until it is executed for all the data obtained in the process S1802 (s1808).
  • the downtime calculation function 152 calculates the average of the downtime obtained for each data extracted in process S1802, and stores this in the prediction result database 162 as the operation base average DT1209.
  • the output management unit 160 extracts necessary data for each screen type from the data stored in the maintenance plan database 161 and the prediction result database 162 in response to a request from the client terminal 190, for example. This data is generated by setting the corresponding screen format (held by the output management unit 160) and outputting it to the client terminal 190.
  • the output management unit 160 may output screen format data to the client terminal 190 to accept a screen configuration customization operation from the user.
  • FIG. 20 shows a screen 2000 displaying a maintenance plan candidate list.
  • each record is listed in ascending or descending order of total cost, average life, history base average DT, and operation base average DT.
  • a radio button 2001 for designating any one of the total cost, the average life, the history base average DT, and the operation base average DT as the sort criterion so that the user can sort the records based on the enumeration criteria.
  • the screen 2000 is included.
  • the user can press the radio button 2001 to perform evaluation between maintenance plan candidates.
  • FIG. 21 shows an example of the procurement / operation plan details and operation display screen 2100 regarding the maintenance plan candidates displayed on the screen 2000 shown in FIG.
  • This screen 2100 is obtained from the data stored in the maintenance plan database 161 and the prediction result database 162 when the output management unit 160 receives a request for detailed display from the client terminal 190 for a certain candidate in the screen 2000 described above.
  • the data necessary for the corresponding screen is extracted, the data is set in the format of the corresponding screen, generated, and output to the client terminal 190.
  • This screen 2100 shows a replacement part used for maintenance work in addition to the plan ID, execution date, machine ID, part number, warehouse, transportation means, delivery date, part price, and transportation cost regarding the target procurement / operation plan.
  • FIG. 22 shows a screen 2200 showing the allocation of expenses related to the maintenance work.
  • This screen 2200 may be used, for example, when a maintenance cost share is determined between a maintenance company and a customer who operates the mining machine 10 in a maintenance contract.
  • the part price can be compared with the other total costs excluding the part price. Good. Therefore, the total cost, the part price, the transportation cost, the operation loss, the operation cost, the work loss, the total cost, the part price, the transportation cost
  • the screen 2200 includes a radio button 2201 for designating any one of the operation loss, work cost, work loss, and user definition as a sorting criterion.
  • the user can press the radio button 2201 to compare the maintenance plan candidates with each other in terms of cost.
  • FIG. 23 is a diagram illustrating a configuration example of the output management unit 2300 that proposes a maintenance plan candidate that is estimated to be optimal for the customer in the present embodiment.
  • the output management unit 2300 includes a maintenance plan evaluation function 2301, a customer policy estimation function 2302, a maintenance plan database 2303, a prediction result database 2304, and a plan evaluation database 2305 (tenth database).
  • the maintenance plan evaluation function 2301 ranks a plurality of maintenance plans from a plurality of evaluation indexes and customer policies.
  • the customer policy estimation function 2302 estimates what maintenance plan the customer tends to select from the maintenance plan selection history of the customer.
  • the maintenance plan database 2303 and the prediction result database 2304 have the same structure as the maintenance plan database 161 and the prediction result database 162, respectively.
  • the plan evaluation database 2305 includes a maintenance plan evaluation table 2400 that stores the result of the maintenance plan evaluation by the maintenance plan evaluation function 2301 and a maintenance selection history table 2410 that is referred to by the customer policy estimation function 2302.
  • the maintenance plan evaluation table 2400 in FIG. 24 stores a plan ID 2401, a maintenance cost 2402 as an evaluation index, an operation base average DT 2403, and a score 2412, a score A 2405, and a score B 2406 calculated by the maintenance plan evaluation function 2301.
  • the score A 2405 is a score obtained by evaluating the maintenance plan candidate by trade-off analysis using the Pareto optimal solution set
  • the score B 2406 is a score obtained by evaluating the maintenance plan candidate based on the degree of coincidence with the customer policy
  • the score 2404 is a score A 2405 and a score B 2406. This is the overall score of the maintenance plan calculated from The calculation formulas for score A, score B, and total score will be described later.
  • the maintenance selection history table 2410 of FIG. 25 includes an abnormality ID 2411 that has occurred in the past, a countermeasure ID 2412 that has been taken for the abnormality, a maintenance plan candidate plan ID 2413 that is displayed to the customer when the abnormality occurs, and a maintenance plan candidate evaluation.
  • the maintenance cost 2402 which is an index, the operation base average DT 2415, and the implementation history 2416 selected by the customer are stored.
  • the maintenance selection history table 2410 links the past abnormality-measures-maintenance plan candidate-maintenance plan selected by the customer.
  • the maintenance plan evaluation function 2301 extracts the maintenance cost, the average life 1207, and the operation base average DT1209 as evaluation indexes from the procurement / operation table 1140 and the prediction result database 2304 of the maintenance plan database 2303, and the maintenance cost, The smaller the operating base average DT 1209 is, the better.
  • normalization is performed so that the maximum value of each evaluation index is 1. By these operations, the best value of each index becomes 1.
  • the maintenance plan evaluation function 2301 obtains a Pareto optimal solution set from a plurality of maintenance plan candidates using three standardized evaluation indexes.
  • An example of obtaining a Pareto optimal solution set using two evaluation axes of maintenance cost and operation base average DT1209 is shown in 2601 of FIG.
  • step S2503 the customer policy estimation function 2302 selects from the maintenance selection history table 2410 what maintenance plan candidates are listed for the abnormality / failure / work combinations that occurred in the past, and the customer selects which maintenance plan from among them. And read out the standardized value of each evaluation index.
  • the customer policy estimation function 2302 estimates the customer policy from the values of the evaluation indexes of the maintenance plan selected by the customer in the past.
  • FIGS. 28 and 2602 show the results of estimating customer policies on two evaluation axes of maintenance cost and operation base average DT using the least square method.
  • step S2505 the maintenance plan evaluation function 2301 obtains a score A from the Pareto optimal solution set and a score B from the estimated customer policy for each maintenance plan candidate, and assigns each score to each maintenance plan candidate using the score A and the score B. A score is assigned and the result is stored in the maintenance plan evaluation table 2400.
  • step S2506 the output management unit 2300 outputs the maintenance plan, prediction result, and maintenance plan evaluation result stored in the maintenance plan database 2303, the prediction result database 2304, and the plan evaluation database 2305 to the user in the order of the score 2404. Wait for the selection.
  • the customer policy estimation function 2302 stores the user's selection in the maintenance selection history table 2410.
  • the estimated customer policy was calculated by standardizing the maintenance cost and operation base average DT values used as evaluation indices (FIG. 28, 2602). This is because it is assumed that the maintenance cost and the operation base average DT vary greatly depending on the parts used for the maintenance work and the work to be performed. However, the maintenance cost and the operation base average DT are about the same as orders for similar parts and operations. Become. In such a case, it is conceivable to estimate the customer policy by using the value of each evaluation index as it is without using normalization. Further, in this embodiment, the procedure for estimating a policy based on the history information of a single customer has been described. For example, if similarity is recognized for each country or region, the history information for each country or region is displayed. Evaluation may be performed collectively, or even if the customer is the same, if the tendency is different for each part such as an engine or a pump, the evaluation may be performed by subdividing each part.
  • replacement parts are not handled unambiguously, and even parts having similar functions are considered for multiple types of parts such as old edition parts and recycled parts, and these various parts are prepared as maintenance plans for work machines. Can be reflected.
  • abnormalities and failures can be estimated from phenomena occurring in work machines, and maintenance operations can be identified accordingly. Therefore, it is possible to estimate failures that may occur in accordance with various situations and identify appropriate maintenance measures. It is possible.
  • parts procurement which is an important consideration in terms of cost and time required for maintenance work in large machines such as mining equipment, taking into account old version parts and remanufactured parts, delivery date, transportation cost, load factor, It is possible to support the planning of multiple patterns of maintenance plans based on the total economic loss according to the load factor.
  • the maintenance planning support technology of this embodiment when an abnormality or failure is detected in the work machine, the old version parts, the refurbished parts, and the procurement plan thereof are also taken into consideration, and what is to be evaluated. Along with this, it can be provided to users to support maintenance planning work.
  • the storage device corresponds to the remaining life of each work machine according to the load factor at the time of failure and the information on the economic loss of the work machine user accompanying the load factor decrease.
  • the arithmetic device collates information on a load factor of a work machine or a part where the phenomenon occurs, included in the information on the phenomenon related to the work machine, with the fifth database.
  • the remaining life of the work machine or the part where the phenomenon occurs is estimated, and the load factor which is reduced according to the extent that the remaining life is less than the grace period from the current time to the maintenance execution candidate date ,
  • the economic loss at the load factor is specified in the fifth database, and the reduced load factor and the economic loss information at the load factor are specified.
  • the maintenance plan in which information further executes a process of outputting to the output device, including a may be. According to this, the load factor that should be reduced to the extent that the failed part can continue to operate until the date of maintenance is identified, and the economic loss on the customer side when the component is operated at this load factor is reported to the user. This makes it possible for the user side to easily consider the customer side in terms of business continuity and economy, which is important when determining a maintenance plan.
  • the storage device stores a sixth database that stores information related to attachment to and removal from the work machine of individual parts or old version parts or regenerated parts thereof.
  • the computing device relates to a part used for the maintenance work or an old version part or a reproduction part thereof indicated by the maintenance plan information, and the same type of part or the old version part or the reproduction thereof in the sixth database.
  • a process of identifying the history of component attachment and removal, calculating the time between the identified attachment and removal as a lifetime, and including the information on the lifetime in the maintenance plan information and outputting to the output device It may be executed. According to this, by presenting to the user how long the replacement parts used in the maintenance plan will have, the user can create a maintenance plan that takes into account, for example, the balance between parts cost and life. It has the effect of making it easier to do
  • the arithmetic unit is related to a part used for the maintenance work indicated by the maintenance plan information or an old version part or a recycled part thereof, and the same type of parts in the first database. Also, the presence or absence of failure information of the old version parts or remanufactured parts is specified, and when the failure information exists, the information on the maintenance work performed for the corresponding failure is specified in the first database, and the corresponding The time between the work start and work end indicated by the maintenance work information is calculated as downtime, and the process of including the downtime information in the maintenance plan information and outputting it to the output device is further executed. There may be.
  • the storage device includes a seventh database storing measured values related to the behavior of the corresponding part by a sensor installed on the part of the work machine, and whether or not the work machine part is in operation. And an eighth database storing conditions of measurement values by the sensor for determining the sensor, and the arithmetic unit relates to a part used for the maintenance work indicated by the maintenance plan information or an old version part or a recycled part thereof
  • the seventh database information on measured values for the same type of part or its old version parts or remanufactured parts is specified, and the information on the specified measured values is collated with the eighth database, and the corresponding part or its old version
  • the downtime of parts or remanufactured parts is calculated as downtime, and the downtime information is stored in the maintenance In which further executes a process of outputting to the output device included in the image information may be.
  • the replacement part adopted in the maintenance plan is shown to the user how much downtime is expected, so on the user side, For example, there is an effect that a maintenance plan considering the balance between parts cost and downtime can be easily made.
  • the storage device further includes a ninth database that stores a schedule of scheduled maintenance scheduled to be performed on the work machine.
  • a ninth database that stores a schedule of scheduled maintenance scheduled to be performed on the work machine.

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Abstract

[Problem] To make it possible to efficiently draft an effective maintenance plan without depending on the skills or the like of a maintenance technician. [Solution] A computer system (100) uses a monitoring system (170) for a work machine (10) to execute the following: a process for receiving information about a phenomenon which occurred in the work machine (10) in a given location, collating the same with a first database, predicting a failure which could occur, and identifying information in the first database and pertaining to maintenance work performed upon occurrence of said failure; a process for collating the identified maintenance-work information with the second database, identifying anticipated standard maintenance-work information, and identifying, in a third database, a period of time during which it is possible to dispatch a technician and equipment for the maintenance work as a maintenance-performance candidate date; and a process for stocking a component, an earlier version thereof or a reusable component specified by the maintenance-work information, identifying, in a fourth database, a warehouse for storage during the delay period until the maintenance-performance candidate date, and the transportation means and transportation cost, when delivering to the given location of the work machine, generating, as maintenance-plan information, information about the maintenance-performance candidate date and information about the deadline, warehouse, transportation means and transportation cost pertaining to the component, the earlier version thereof or the reusable component to be used in the maintenance work, and outputting the same to an output device (111).

Description

保守計画立案支援システム、保守計画立案支援方法、保守計画立案支援プログラムMaintenance planning support system, maintenance planning support method, maintenance planning support program
　本発明は、保守計画立案支援システム、保守計画立案支援方法、保守計画立案支援プログラムに関する。 The present invention relates to a maintenance planning support system, a maintenance planning support method, and a maintenance planning support program.
　プラントや各種作業機械、各種輸送機器などの保守業務に於いては、対象物が故障、停止する以前にその予兆を観測して保守する予知保守（状態監視保全）が重要である。近年では、センシング機器の性能向上やネットワークの改善、処理装置の高性能化によって、保守対象の状態をリアルタイムにモニタリングすることが可能となった。また、そうして得られるモニタリング結果を保守業務に活用することも可能となってきている。そうした技術として、以下の技術が提案されている。 In the maintenance work of plants, various work machines, various transport equipment, etc., predictive maintenance (condition monitoring maintenance) in which the object is observed and maintained before the object breaks down or stops is important. In recent years, it has become possible to monitor the status of maintenance objects in real time by improving the performance of sensing devices, improving the network, and improving the performance of processing devices. It is also possible to utilize the monitoring results obtained in this way for maintenance work. The following technologies have been proposed as such technologies.
　すなわち、類似性の高い保守事例情報が十分に存在しない場合においても、データの曖昧性や一部の欠如部分を含む保守事例情報を有効に活用した故障診断システムを提供するための、故障診断システム（特許文献１参照）などが提案されている。 That is, a failure diagnosis system for providing a failure diagnosis system that effectively uses maintenance case information including data ambiguity and some missing parts even when there is not enough similar maintenance case information. (Refer patent document 1) etc. are proposed.
　加えて、保守業務を迅速かつ安価に実施するためには部品や保守員の調達を含む、効率的な保守計画の立案も重要である。これに関して、作業機械の寿命をより正確に予測し、適切なオーバホール実施計画を早期に立案可能とするための、作業機械のメンテナンス作業管理システム（特許文献２参照）なども提案されている。 In addition, in order to carry out maintenance work quickly and inexpensively, it is also important to create an efficient maintenance plan, including the procurement of parts and maintenance personnel. In this regard, a work machine maintenance work management system (see Patent Document 2) and the like for predicting the life of a work machine more accurately and making an appropriate overhaul implementation plan early can be proposed.
特開２０１１－１７０７２４号公報JP 2011-170724 A 特開２００７－１００３０５号公報JP 2007-100305 A
　近年、資源需要の高まりによって鉱山機械の市場は急速に拡大している。そうした鉱山機械の保守事業に於いて、機械稼働率の最大化を実現するためには、世界各地に点在する鉱山（消費地）と鉱山機械や部品等の倉庫（供給地）とを結ぶグローバルサプライチェーンの最適化が必要となってきている。鉱山機械の部品は一般的に高価かつ大型であり、保守を実施するに当たって、どの倉庫から、どのような輸送手段を用いて部品を調達するかが問題となる。 In recent years, the market for mining equipment has been expanding rapidly due to rising demand for resources. In such a mining equipment maintenance business, in order to maximize the machine operation rate, a global connection between mines (consumption areas) scattered around the world and warehouses (supply areas) for mining equipment and parts, etc. Supply chain optimization is becoming necessary. Mining machine parts are generally expensive and large, and in carrying out maintenance, it becomes a problem which warehouse should be used to procure parts from which warehouse.
　また、鉱山機械のライフサイクルコスト最適化には、一度故障した部品を修理、再生した再生品の有効利用が求められている。 Also, to optimize the life cycle cost of mining equipment, it is required to effectively use recycled products that have been repaired and refurbished once a failed part.
　このように、今日において鉱山機械の保守計画の立案は、広範な知識と経験が必要で、高度なスキルを要する業務といえる。現在、こうした高度な計画立案業務は、優秀な保守計画立案者に依存する、属人的業務である。一方、市場の急拡大によって、保守員の人員不足、経験不足が問題となっており、上述した計画立案業務を効率的に進めることが困難になっている。これらの課題を特許文献１および特許文献２記載の技術で解決することは出来ない。 Thus, it can be said that planning a maintenance plan for a mining machine today requires a wide range of knowledge and experience and requires advanced skills. Currently, these advanced planning tasks are personal tasks that depend on excellent maintenance planners. On the other hand, due to the rapid expansion of the market, the lack of maintenance staff and lack of experience has become a problem, making it difficult to efficiently carry out the above-mentioned planning work. These problems cannot be solved by the techniques described in Patent Document 1 and Patent Document 2.
　そこで本発明の目的は、保守員のスキル等に依存せずに効果的な保守計画を効率的に立案可能とする技術を提供することにある。 Therefore, an object of the present invention is to provide a technique that enables an effective maintenance plan to be efficiently formulated without depending on the skills of maintenance personnel.
　上記課題を解決する本発明の保守計画立案支援システムは、作業機械に発生した現象の情報と、該当現象の発生後に作業機械に生じた故障の情報と、故障に対して実施された保守作業の情報と、を対応づけて保持する第１データベースと、作業機械別に規定された標準の保守作業の情報を保持する第２データベースと、保守作業用の各人員および各機材の稼働可能時期の情報を保持する第３データベースと、保守作業に用いられる部品ないしその旧版部品または再生部品の各在庫と価格、および、輸送先別および輸送手段別の納期および輸送コストの情報とを、倉庫別に保持する第４データベースと、を格納する記憶装置と、作業機械の監視システムより、ある所在地の作業機械に発生した現象の情報をネットワークを介して受信し、受信した現象の情報を第１データベースに照合して、該当現象の発生後に起こり得る故障を推定し、故障に際して作業機械に実施された保守作業の情報を第１データベースにて特定する処理と、特定した保守作業の情報を第２データベースに照合し、作業機械に対して実施が予想される標準の保守作業の情報を特定し、保守作業の情報が指定する保守作業用の人員および機材の稼働可能時期を保守実施候補日として第３データベースで特定する処理と、保守作業の情報が指定する部品ないしその旧版部品または再生部品を在庫し、部品ないしその旧版部品または再生部品を、作業機械の所在地に輸送手段により納品する場合の納期が、現時点から保守実施候補日までの猶予期間に収まる倉庫と、納期に対応した輸送手段及びその輸送コストを、第４データベースで特定し、特定した、保守作業に用いる部品ないしその旧版部品または再生部品に関する、納期、倉庫、輸送手段、及び輸送コストの情報と保守実施候補日の情報とを保守計画情報として生成し出力装置に出力する処理と、を実行する演算装置と、を備えることを特徴とする。 The maintenance planning support system of the present invention that solves the above problems includes information on a phenomenon that has occurred in the work machine, information on a failure that has occurred in the work machine after the occurrence of the phenomenon, and maintenance work that has been performed for the failure. A first database that holds information in association with each other, a second database that holds information on standard maintenance work defined for each work machine, and information on the availability of each person and equipment for maintenance work The third database to be held, each stock and price of parts used for maintenance work or its old version parts or remanufactured parts, and delivery date and transportation cost information for each transportation destination and transportation means are retained for each warehouse. Receives information on phenomena that occurred in a work machine in a certain location from a storage device that stores 4 databases and a work machine monitoring system, and receives them The first database is used to collate the information on the phenomenon, the failure that may occur after the occurrence of the corresponding phenomenon is estimated, and the information on the maintenance work performed on the work machine at the time of the failure is identified in the first database. The maintenance work information is collated with the second database, the standard maintenance work information expected to be carried out on the work machine is specified, and the maintenance work personnel and equipment availability specified by the maintenance work information Is identified in the third database as the maintenance implementation candidate date, and the parts specified by the maintenance work information or its old version parts or remanufactured parts are in stock, and the parts or their old version parts or regenerated parts are transported to the location of the work machine If the delivery date for delivery by means is within the grace period from the current time to the maintenance candidate date, the transportation means corresponding to the delivery date and its transportation cost Generate and output information on delivery dates, warehouses, transportation means, transportation costs and maintenance candidate dates as maintenance plan information for the parts to be used for maintenance work or the old version parts or recycled parts specified in the database. And a processing device that executes processing to be output to the device.
　本発明によれば、保守員のスキル等に依存せずに効果的な保守計画を効率的に立案可能となる。 According to the present invention, it is possible to efficiently formulate an effective maintenance plan without depending on the skills of maintenance personnel.
本実施形態における保守計画立案支援システムを含むネットワーク構成図である。It is a network block diagram including the maintenance plan planning support system in this embodiment. 本実施形態における現象履歴データベース例を示す図である。It is a figure which shows the example of a phenomenon log | history database in this embodiment. 本実施形態における及び故障履歴データベース例を示す図である。It is a figure which shows the example of a failure log | history database in this embodiment. 本実施形態における作業履歴データベース例を示す図である。It is a figure which shows the work log | history database example in this embodiment. 本実施形態における保守作業データベース例を示す図である。It is a figure which shows the example of a maintenance work database in this embodiment. 本実施形態における日程データベース例を示す図である。It is a figure which shows the example of a schedule database in this embodiment. 本実施形態の部品調達データベースにおける部品在庫テーブル例を示す図である。It is a figure which shows the example of a components inventory table in the components procurement database of this embodiment. 本実施形態の部品調達データベースにおける輸送手段テーブル例を示す図である。It is a figure which shows the example of a transportation means table in the components procurement database of this embodiment. 本実施形態の部品調達データベースにおける互換部品テーブル例を示す図である。It is a figure which shows the example of a compatible components table in the components procurement database of this embodiment. 本実施形態の顧客知識データベースにおける運用ロステーブル例を示す図である。It is a figure which shows the example of the operation | movement loss table in the customer knowledge database of this embodiment. 本実施形態の顧客知識データベースにおける残寿命テーブル例を示す図である。It is a figure which shows the example of a remaining life table in the customer knowledge database of this embodiment. 本実施形態の顧客知識データベースにおける顧客テーブル例を示す図である。It is a figure which shows the example of a customer table in the customer knowledge database of this embodiment. 本実施形態における定期保守データベース例を示す図である。It is a figure which shows the example of a regular maintenance database in this embodiment. 本実施形態における部品履歴データベース例を示す図である。It is a figure which shows the example of a components log | history database in this embodiment. 本実施形態の部品稼動データベースにおける稼働判定テーブル例を示す図である。It is a figure which shows the operation determination table example in the components operation database of this embodiment. 本実施形態の部品稼動データベースにおける稼働実績テーブル例を示す図である。It is a figure which shows the example of an operation performance table in the component operation database of this embodiment. 本実施形態の保守計画データベースにおける異常診断結果テーブル例を示す図である。It is a figure which shows the example of an abnormality diagnosis result table in the maintenance plan database of this embodiment. 本実施形態の保守計画データベースにおける対策抽出結果テーブル例を示す図である。It is a figure which shows the example of a countermeasure extraction result table in the maintenance plan database of this embodiment. 本実施形態の保守計画データベースにおける実施候補テーブル例を示す図である。It is a figure which shows the example of an implementation candidate table in the maintenance plan database of this embodiment. 本実施形態の保守計画データベースにおける調達／運用計画テーブル例を示す図である。It is a figure which shows the example of a procurement / operation plan table in the maintenance plan database of this embodiment. 本実施形態の予測結果データベース例を示す図である。It is a figure which shows the example of a prediction result database of this embodiment. 本実施形態における保守計画立案支援方法の手順例１を示すフロー図である。It is a flowchart which shows the procedure example 1 of the maintenance plan planning assistance method in this embodiment. 本実施形態における保守計画立案支援方法の手順例２を示すフロー図である。It is a flowchart which shows the procedure example 2 of the maintenance plan planning assistance method in this embodiment. 本実施形態における保守計画立案支援方法の手順例３を示すフロー図である。It is a flowchart which shows the example 3 of a procedure of the maintenance plan planning assistance method in this embodiment. 本実施形態における保守計画立案支援方法の手順例４を示すフロー図である。It is a flowchart which shows the procedure example 4 of the maintenance plan planning assistance method in this embodiment. 本実施形態における保守計画立案支援方法の手順例５を示すフロー図である。It is a flowchart which shows the example 5 of a procedure of the maintenance plan planning assistance method in this embodiment. 本実施形態における保守計画立案支援方法の手順例６を示すフロー図である。It is a flowchart which shows the example 6 of a procedure of the maintenance plan planning assistance method in this embodiment. 本実施形態における残寿命と保守計画実施候補との関係を示す図である。It is a figure which shows the relationship between the remaining life in this embodiment, and a maintenance plan execution candidate. 本実施形態における残寿命と負荷率と運用ロスの関係を示す図である。It is a figure which shows the relationship between the remaining life in this embodiment, a load factor, and an operation loss. 本実施形態における出力画面例１を示す図である。It is a figure which shows the example 1 of an output screen in this embodiment. 本実施形態における出力画面例２を示す図である。It is a figure which shows the example 2 of an output screen in this embodiment. 本実施形態における出力画面例３を示す図である。It is a figure which shows the example 3 of an output screen in this embodiment. 本実施形態における保守計画評価を実行する出力管理部を示す図である。It is a figure which shows the output management part which performs the maintenance plan evaluation in this embodiment. 本実施形態の計画評価データベースにおける保守計画評価テーブル例を示す図である。It is a figure which shows the example of a maintenance plan evaluation table in the plan evaluation database of this embodiment. 本実施形態の計画評価データベースにおける保守選択履歴テーブル例を示す図である。It is a figure which shows the example of a maintenance selection log | history table in the plan evaluation database of this embodiment. 本実施形態における保守計画評価方法の手順例を示すフロー図である。It is a flowchart which shows the example of a procedure of the maintenance plan evaluation method in this embodiment. 本実施形態における保守計画候補とパレート最適解集合の関係を示す図である。It is a figure which shows the relationship between the maintenance plan candidate and Pareto optimal solution set in this embodiment. 本実施形態における顧客選択履歴と推定顧客ポリシーの関係を示す図である。It is a figure which shows the relationship between the customer selection log | history and estimated customer policy in this embodiment. 本実施形態における保守計画評価用変数Ｌ１とＬ２の関係を示す図である。It is a figure which shows the relationship between the variables L1 and L2 for maintenance plan evaluation in this embodiment.
　以下に本発明の実施形態について図面を用いて詳細に説明する。図１は本実施形態の　保守計画立案システム１００を含むネットワーク構成例を示す図である。図１に示す保守計画立案システム１００は、保守員のスキル等に依存せずに効果的な保守計画を効率的に立案可能とするためのコンピュータシステムである。 Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a diagram showing a network configuration example including a maintenance plan planning system 100 according to the present embodiment. A maintenance plan planning system 100 shown in FIG. 1 is a computer system that enables an effective maintenance plan to be efficiently planned without depending on the skills of maintenance personnel.
　本実施形態では、作業機械の例として、大型の鉱山機械をとりあげて説明を行うものとする。この鉱山機械は、すでに上述したように、世界的な資源需要の高まりによって市場が急拡大する一方、機械メーカー側での部品供給能力が市場需要に追い付いていない現状がある。他方、鉱山機械の保守に際しては、最新版である新品の部品、遡及対策が未実施である古いバージョンの旧版部品、および、故障した部品を修理、再生した再生部品といった、共通に使用できる程度に機能は同一ながら、状態や由来の異なる様々な部品らを、熟練した保守員らが使い分けることがなされていた。しかしながら、そうした様々な属性の部品らは、世界規模で点在する倉庫に保管されており、未熟な保守員が属性や所在、在庫等を的確に認識し、鉱山機械保守計画へ適切に取り入れることは従来困難であった。本実施形態の保守計画立案支援システム１００は、未熟な保守員でも、上述した様々な属性の部品を的確に採用し、複雑な保守計画を適切に立案可能とするものとなる。 In this embodiment, a large mining machine will be described as an example of a work machine. As described above, the market for this mining machine has expanded rapidly due to the increase in global demand for resources, while the parts supply capacity on the machine manufacturer side has not caught up with the market demand. On the other hand, when maintaining mining equipment, new parts that are the latest version, old version parts that have not been retroactively implemented, and refurbished parts that have repaired and refurbished faulty parts can be used in common. Skilled maintenance staff used various parts with the same functions but different states and origins. However, parts with such various attributes are stored in warehouses scattered around the world, and unskilled maintenance personnel accurately recognize attributes, locations, inventory, etc., and appropriately incorporate them into the mining equipment maintenance plan. Has been difficult in the past. The maintenance plan planning support system 100 according to the present embodiment enables even an inexperienced maintenance staff to appropriately adopt the above-described components having various attributes and appropriately plan a complicated maintenance plan.
　こうした保守計画立案支援システム１００はネットワーク１８０に接続され、監視システム１７０、およびクライアント端末１９０とデータ通信可能となっている。なお、監視システム１７０は、鉱山機械１０に設置されたセンサ１１での計測値すなわちセンサデータを監視するか、あるいは、センサデータを適宜なアルゴリズムに適用して算出したパラメータを監視して、鉱山機械１０における異常を検出するコンピュータシステムである。監視システム１７０は、センサデータあるいは算出したパラメータが、所定の閾値を超えることをトリガとして異常を検出し、その異常検出の旨を保守計画立案支援システム１００に送信する。上述のセンサ１１は、例えば、鉱山機械１０のモータ回転数、ポンプ内圧、各所の温度や振動等を計測するセンサを想定できる。また、鉱山機械１０に設置されたセンサ１１は、鉱山機械１０の備える通信装置、あるいはセンサ１１自身が備える通信装置を利用して、センサデータを監視システム１７０に送信している。 The maintenance planning support system 100 is connected to the network 180 and can communicate data with the monitoring system 170 and the client terminal 190. The monitoring system 170 monitors the measurement value of the sensor 11 installed in the mining machine 10, that is, sensor data, or monitors the parameter calculated by applying the sensor data to an appropriate algorithm, and 10 is a computer system for detecting an abnormality in 10. The monitoring system 170 detects an abnormality when the sensor data or the calculated parameter exceeds a predetermined threshold, and transmits a notification of the abnormality detection to the maintenance planning support system 100. The sensor 11 described above can be assumed to be a sensor that measures, for example, the motor rotation speed of the mining machine 10, the pump internal pressure, and the temperature and vibration of each place. The sensor 11 installed in the mining machine 10 transmits sensor data to the monitoring system 170 by using a communication device provided in the mining machine 10 or a communication device provided in the sensor 11 itself.
　一方、クライアント端末１９０は、保守計画立案支援システム１００にアクセスし、キーボードやマウス等における、保守員からのデータ入力の受付処理や、保守計画立案支援システム１００から得たデータのディスプレイ等における表示処理、の各処理を担っている。 On the other hand, the client terminal 190 accesses the maintenance plan planning support system 100, receives data input from maintenance personnel using a keyboard, a mouse, and the like, or displays data obtained from the maintenance plan planning support system 100 on a display or the like. It is responsible for each processing.
　また、保守計画立案システム１００のハードウェア構成は以下の如くとなる。保守計画立案システム１００は、ハードディスクドライブなど適宜な不揮発性記憶装置で構成される記憶装置１１５、ＲＡＭなど揮発性記憶装置で構成されるメモリ１１３、記憶装置１１５に保持されるプログラムをメモリ１１３に読み出すなどして実行しシステム自体の統括制御を行なうとともに各種判定、演算及び制御処理を行なうＣＰＵ１１４（演算装置）、ネットワーク１８０と接続し他装置との通信処理を担う通信装置１１２を備える。 Also, the hardware configuration of the maintenance planning system 100 is as follows. The maintenance planning system 100 reads out to the memory 113 a storage device 115 composed of a suitable non-volatile storage device such as a hard disk drive, a memory 113 composed of a volatile storage device such as RAM, and a program held in the storage device 115. The CPU 114 (arithmetic unit) for performing overall control of the system itself and performing various determinations, computations and control processes, and the communication device 112 connected to the network 180 and responsible for communication processing with other devices.
　上述したプログラムの実行により実装される機能としては、現象診断機能１２１、故障診断機能１２２、対策抽出機能１３１、計画作成機能１４１、定期保守計画調整機能１４２、寿命算出機能１５１、ダウンタイム算出機能１５２、がある。図１で示す保守計画立案支援システム１００の例では、こうした各機能群と、各機能が利用するデータを記憶するデータベース群とをセットにした機能部を例示している。この機能部としては、異常診断部１２０、対策抽出部１３０、計画作成部１４０、性能予測部１５０、および出力管理部１６０がある。各機能部間のデータの伝送はＢＵＳを介してＩ／Ｏ１１１によって管理される。各機能部のデータベースについては後述する。 Functions implemented by executing the above-described programs include a phenomenon diagnosis function 121, a failure diagnosis function 122, a countermeasure extraction function 131, a plan creation function 141, a periodic maintenance plan adjustment function 142, a life calculation function 151, and a downtime calculation function 152. There is. In the example of the maintenance planning support system 100 shown in FIG. 1, a functional unit in which each of these function groups and a database group storing data used by each function is illustrated. The functional units include an abnormality diagnosis unit 120, a countermeasure extraction unit 130, a plan creation unit 140, a performance prediction unit 150, and an output management unit 160. Data transmission between the functional units is managed by the I / O 111 via the BUS. The database of each functional unit will be described later.
　なお、本実施形態では、クライアント端末１９０によってデータ入出力を行うことを想定しているが、保守計画立案支援システム１００が入出力機能及びデバイス（ディスプレイやキーボード等）を有しているとしてもよい。 In the present embodiment, it is assumed that data input / output is performed by the client terminal 190, but the maintenance planning support system 100 may have an input / output function and a device (display, keyboard, etc.). .
　続いて、本実施形態の保守計画立案システム１００が備える機能について説明する。上述したように、以下に説明する機能は、例えば保守計画立案支援システム１００が備えるプログラムを実行することで実装される機能と言える。なお、ここでの説明におけるデータベースの詳細については後述する。 Subsequently, functions provided in the maintenance plan planning system 100 of the present embodiment will be described. As described above, the functions described below can be said to be functions implemented by executing a program provided in the maintenance plan planning support system 100, for example. Details of the database in this description will be described later.
　保守計画立案支援システム１００は、監視システム１７０より、ある所在地の鉱山機械１０に発生した現象の情報をネットワーク１８０を介して受信し、当該受信した現象の情報を、現象履歴データベース１２３や故障履歴データベース１２４（いずれも第１データベース）に照合して、該当現象の発生後に起こり得る故障を推定し、該当故障に際して鉱山機械１０に実施された保守作業の情報を作業履歴データベース１３２（第１データベース）にて特定する機能を有する。 The maintenance planning support system 100 receives information on a phenomenon that has occurred in the mining machine 10 at a certain location from the monitoring system 170 via the network 180, and uses the phenomenon history database 123 and the failure history database as information on the received phenomenon. 124 (both are the first database), a failure that may occur after the occurrence of the corresponding phenomenon is estimated, and information on the maintenance work performed on the mining machine 10 at the time of the corresponding failure is stored in the work history database 132 (first database). Specific functions.
　また、保守計画立案支援システム１００は、上述にて特定した保守作業の情報を保守作業データベース１３３（第２データベース）に照合し、上述した鉱山機械１０に対して実施が予想される標準の保守作業の情報を特定し、該当保守作業の情報が指定する保守作業用の人員および機材の稼働可能時期を保守実施候補日として日程データベース１４３（第３データベース）で特定する機能を有する。 Further, the maintenance planning support system 100 collates the maintenance work information specified above with the maintenance work database 133 (second database), and standard maintenance work expected to be performed on the mining machine 10 described above. And the maintenance work candidate specified by the maintenance work information and the time when the equipment can be operated are specified in the schedule database 143 (third database) as the maintenance execution candidate date.
　また、保守計画立案支援システム１００は、上述した保守作業の情報が指定する部品ないしその旧版部品または再生部品を在庫し、該当部品ないしその旧版部品または再生部品を、該当鉱山機械１０の所在地に輸送手段により納品する場合の納期が、現時点から保守実施候補日までの猶予期間に収まる倉庫と、納期に対応した輸送手段及びその輸送コストを、部品調達データベース１４４（第４データベース）で特定し、当該特定した、保守作業に用いる部品ないしその旧版部品または再生部品に関する、納期、倉庫、輸送手段、及び輸送コストの情報と、保守実施候補日の情報とを保守計画情報として生成し、保守計画データベース１６１に格納するか、あるいはクライアント端末１９０に出力する機能を有する。 In addition, the maintenance planning support system 100 stocks the parts specified by the above-described maintenance work information or the old version parts or the recycled parts thereof, and transports the corresponding parts or the old version parts or the recycled parts to the location of the corresponding mining machine 10. Specify the warehouse in which the delivery date for delivery by means falls within the grace period from the current time to the candidate date of maintenance, the transportation means corresponding to the delivery date and the transportation cost in the parts procurement database 144 (fourth database), and Information on delivery date, warehouse, transportation means and transportation cost and information on the maintenance execution candidate date related to the specified parts used for maintenance work or its old version parts or recycled parts, and maintenance execution candidate date information are generated as maintenance plan information 161. Or has a function of outputting to the client terminal 190.
　また、保守計画立案支援システム１００は、鉱山機械１０に関する現象の情報（監視システム１７０由来のもの）が含む、鉱山機械１０ないし現象発生箇所の部品の負荷率の情報を、顧客知識データベース１４５（第５データベース）に照合して、鉱山機械１０ないし現象発生箇所の部品の残寿命を推定し、当該残寿命が、現時点から保守実施候補日までの猶予期間を下回る程度に応じて低下させた負荷率を該当鉱山機械１０における負荷率とし、当該負荷率での経済損失を顧客知識データベース１４５（第５データベース）にて特定し、低下させた負荷率および当該負荷率での経済損失の情報を、上述した保守計画情報に含めて保守計画データベース１６１に格納するか、クライアント端末１９０に出力する機能を有する。 Further, the maintenance planning support system 100 uses the customer knowledge database 145 (No. 1) as the load factor information of the mining machine 10 or the part where the phenomenon occurs, which is included in the information on the phenomenon related to the mining machine 10 (from the monitoring system 170). 5 database), estimated the remaining life of the mining machine 10 or the part where the phenomenon occurred, and reduced the load factor according to the extent that the remaining life is less than the grace period from the current time to the maintenance candidate date. Is the load factor in the corresponding mining machine 10, the economic loss at the load factor is specified in the customer knowledge database 145 (fifth database), and the reduced load factor and the information on the economic loss at the load factor are described above. It is included in the maintenance plan information included in the maintenance plan database 161 or output to the client terminal 190.
　また、保守計画立案支援システム１００は、上述した保守計画情報が示す、保守作業に用いる部品ないしその旧版部品または再生部品に関し、部品履歴データベース１５３（第６データベース）における、同一種類の部品ないしその旧版部品または再生部品の取付と取外の履歴を特定し、当該特定した取付と取外との間の時間を寿命として算出し、当該寿命の情報を保守計画情報に含めて保守計画データベース１６１に格納するか、あるいはクライアント端末１９０に出力する機能を有する。 Further, the maintenance planning support system 100 relates to a part used for maintenance work or an old version part or a reproduced part indicated by the above-described maintenance plan information, in the part history database 153 (sixth database), the same type of part or an old version thereof. The history of the attachment and removal of the part or the remanufactured part is specified, the time between the specified attachment and removal is calculated as the life, and the life information is included in the maintenance plan information and stored in the maintenance plan database 161 Or a function of outputting to the client terminal 190.
　また、保守計画立案支援システム１００は、上述した保守計画情報が示す、保守作業に用いる部品ないしその旧版部品または再生部品に関し、故障履歴データベース１２４（第１データベース）における、同一種類の部品ないしその旧版部品または再生部品の、故障の情報の有無を特定し、故障の情報が存在する場合に、該当故障に対して実施された保守作業の情報を作業履歴データベース１３２（第１データベース）にて特定し、該当保守作業の情報が示す、作業開始から作業終了までの間の時間をダウンタイムとして算定し、当該ダウンタイムの情報を、上述した保守計画情報に含めて保守計画データベース１６１に格納するか、あるいはクライアント端末１９０に出力する機能を有する。 In addition, the maintenance planning support system 100 relates to a part used for maintenance work or an old version part or a reproduction part thereof indicated by the above-described maintenance plan information, and the same type of part or an old version thereof in the failure history database 124 (first database). The presence or absence of failure information on the part or the remanufactured part is specified, and when the failure information exists, the maintenance work information performed for the failure is specified in the work history database 132 (first database). The time from the start of work to the end of work indicated by the information of the corresponding maintenance work is calculated as downtime, and the downtime information is included in the maintenance plan information described above and stored in the maintenance plan database 161, Alternatively, it has a function of outputting to the client terminal 190.
　また、保守計画立案支援システム１００は、上述した保守計画情報が示す、保守作業に用いる部品ないしその旧版部品または再生部品に関し、部品稼働データベース１５４の稼働実績テーブル１０１０（第７データベース）における、同一種類の部品ないしその旧版部品または再生部品についての測定値の情報を特定し、当該特定した測定値の情報を部品稼働データベース１５４の稼働判定テーブル１０００（第８データベース）に照合して、該当部品ないしその旧版部品または再生部品の稼働停止期間をダウンタイムとして算定し、当該ダウンタイムの情報を、上述した保守計画情報に含めて保守計画データベース１６１に格納するか、あるいはクライアント端末１９０に出力する機能を有する。 In addition, the maintenance planning support system 100 relates to a part used for maintenance work or an old version part or a recycled part indicated by the maintenance plan information described above, in the operation result table 1010 (seventh database) of the part operation database 154. Information on the measured value of the part or its old version part or recycled part is specified, and the information on the specified measured value is collated with the operation determination table 1000 (eighth database) of the part operation database 154, It has the function of calculating the downtime of the old version parts or the recycled parts as downtime and storing the downtime information in the maintenance plan database 161 included in the above-mentioned maintenance plan information or outputting it to the client terminal 190 .
　また、保守計画立案支援システム１００は、現時点から、上述した保守計画情報が示す保守実施候補日までの期間内に、定期保守の日程が含まれているか、定期保守データベース１４６（第９データベース）にて判定し、保守計画情報が示す保守実施候補日までの期間内に定期保守の日程が含まれている場合、該当定期保守日で保守実施候補日を置換し、保守計画情報の生成を再度実行する機能を有する。 In addition, the maintenance plan formulation support system 100 determines whether the regular maintenance schedule is included in the regular maintenance database 146 (the ninth database) within the period from the present time to the maintenance execution candidate date indicated by the above-described maintenance plan information. If the scheduled maintenance schedule is included in the period until the maintenance execution candidate date indicated by the maintenance plan information, replace the maintenance execution candidate date with the corresponding scheduled maintenance date, and generate maintenance plan information again. It has the function to do.
　こうした機能を、図１の機能部に対応づけて、より具体的に説明すると以下の通りとなる。保守計画立案支援システム１００が備える異常診断部１２０は、現象診断機能１２１によって、鉱山機械１０らに過去生じた現象履歴を蓄積した現象履歴データベース１２２と、監視システム１７０から受信したセンサデータ若しくはパラメータとを参照比較することで、監視システム１７０が鉱山機械１０について検出した異常がどのような現象であるかを診断する。 These functions will be described in more detail in association with the functional units shown in FIG. 1 as follows. The abnormality diagnosis unit 120 included in the maintenance planning support system 100 includes a phenomenon history database 122 that stores phenomenon histories that have occurred in the mining machine 10 and the like, and sensor data or parameters received from the monitoring system 170 by the phenomenon diagnosis function 121. By referring to and comparing, it is diagnosed what kind of phenomenon the abnormality detected by the monitoring system 170 for the mining machine 10 is.
　また、保守計画立案支援システム１００が備える異常診断部１２０は、故障診断機能１２２によって、鉱山機械１０らに過去生じた故障履歴を蓄積した故障履歴データベース１２４と、現象診断機能１２１が特定した現象とを参照比較することで、現象診断機能１２１が特定した現象がどのような故障の前兆であるかを診断する。 In addition, the abnormality diagnosis unit 120 included in the maintenance planning support system 100 includes a failure history database 124 that stores failure histories that have occurred in the mining machine 10 and the like using the failure diagnosis function 122, and the phenomenon that the phenomenon diagnosis function 121 identifies. By referring to and comparing the above, it is diagnosed what kind of failure the phenomenon specified by the phenomenon diagnosis function 121 is.
　こうして保守計画立案支援システム１００の異常診断部１２０は、上述した機能群によって、監視システム１７０が検出した異常がどのような故障に結びつくかを特定する。また、異常診断部１２０の現象診断機能１２１及び故障診断機能１２２は、特定した現象及び故障を出力管理部１６０に送信する。出力管理部１６０は、これを保守計画データベース１６１に格納し管理する。 In this way, the abnormality diagnosis unit 120 of the maintenance planning support system 100 identifies what kind of failure the abnormality detected by the monitoring system 170 is associated with using the above-described function group. In addition, the phenomenon diagnosis function 121 and the failure diagnosis function 122 of the abnormality diagnosis unit 120 transmit the specified phenomenon and failure to the output management unit 160. The output management unit 160 stores and manages this in the maintenance plan database 161.
　また、保守計画立案支援システム１００が備える対策抽出部１３０は、対策抽出機能１３１によって、鉱山機械１０らの過去の故障に対する保守作業履歴を蓄積した作業履歴データベース１３２と、故障診断機能１２２が特定した故障とを参照比較することで、監視システム１７０が検出した異常に対して、取るべき対策を抽出する。また、対策抽出機能１３１は、保守作業データベース１３３を参照して、作業履歴データベース１３２から抽出した保守作業の実施に必要な保守員技能や機材、作業費用や作業時間を抽出する。また、対策抽出機能１３１は、上述で抽出した、取るべき対策の情報を出力管理部１６０に送信する。出力管理部１６０は、これを保守計画データベース１６１に格納し管理する。 In addition, the countermeasure extraction unit 130 included in the maintenance planning support system 100 uses the countermeasure extraction function 131 to identify the work history database 132 in which maintenance work histories for past failures of the mining machine 10 and the like and the failure diagnosis function 122 have identified. By comparing the failure with the reference, a countermeasure to be taken is extracted for the abnormality detected by the monitoring system 170. Further, the countermeasure extracting function 131 refers to the maintenance work database 133 and extracts maintenance staff skills, equipment, work costs, and work time necessary for performing the maintenance work extracted from the work history database 132. In addition, the measure extraction function 131 transmits information on measures to be taken, extracted as described above, to the output management unit 160. The output management unit 160 stores and manages this in the maintenance plan database 161.
　また、保守計画立案支援システム１００が備える計画作成部１４０は、計画作成機能１４１によって、上述した対策抽出部１３０が抽出した対策に関して、日程データベース１４３から該当する保守員、機材のスケジュールを抽出して保守実施候補日を抽出する。なお、対策抽出部１３０が抽出した対策が、部品交換を含む保守作業であった場合、計画作成機能１４１は、部品調達データベース１４４を参照し、調達に係る調達計画を立案する。この立案手順の詳細については後述する。また、計画作成機能１４１は、顧客知識データベース１４５における、運用ロス情報および残寿命情報から運用計画を立案する。この立案手順の詳細については後述する。また、計画作成部１４０における定期保守計画調整機能１４２は、定期保守データベース１４６の定期保守日程と、前述の調達計画及び運用計画の日程を調整し、保守日程の調整を行う。この調整手順の詳細については後述する。 In addition, the plan creation unit 140 included in the maintenance plan planning support system 100 extracts a schedule of maintenance personnel and equipment corresponding to the measures extracted by the above-described measure extraction unit 130 from the schedule database 143 by the plan creation function 141. Extract maintenance candidate dates. When the countermeasure extracted by the countermeasure extracting unit 130 is a maintenance operation including parts replacement, the plan creation function 141 refers to the parts procurement database 144 and drafts a procurement plan for procurement. Details of this planning procedure will be described later. Further, the plan creation function 141 drafts an operation plan from the operation loss information and the remaining life information in the customer knowledge database 145. Details of this planning procedure will be described later. In addition, the regular maintenance plan adjustment function 142 in the plan creation unit 140 adjusts the maintenance schedule by adjusting the regular maintenance schedule in the regular maintenance database 146 and the schedule of the above-described procurement plan and operation plan. Details of this adjustment procedure will be described later.
　これら、計画作成機能１４１、定期保守計画調整機能１４２らによって立案された運用計画及び調達計画から成る保守計画に関する情報は、計画作成部１４０によって出力管理部１６０に送信される。出力管理部１６０は、これを保守計画データベース１６１に格納し管理する。 The information related to the maintenance plan composed of the operation plan and the procurement plan prepared by the plan creation function 141 and the regular maintenance plan adjustment function 142 is transmitted to the output management unit 160 by the plan creation unit 140. The output management unit 160 stores and manages this in the maintenance plan database 161.
　また、対策抽出部１３０によって、取るべき対策として部品交換の必要が認められ、また、その部品交換に関して、計画作成部１４０において使用部品が特定されたとき、性能予測部１５０における寿命算出機能１５１は、部品履歴データベース１５３に記録されている各部品に対する作業の履歴を参照して、使用部品の寿命実績を算出する。また、寿命算出機能１５１は、算出した寿命実績の情報を出力管理部１６０に送信する。出力管理部１６０は、これを予測結果データベース１６２に格納し管理する。また、このとき、性能予測部１５０のダウンタイム算出機能１５２は、部品履歴データベース１５３において、ステータスが交換済みとなっている、すなわち寿命をすでに迎えている部品を特定し、その部品の故障履歴を故障履歴データベース１２４から読み込み、その故障履歴にもとづいたダウンタイムを算出する。この算出手法の詳細は後述する。また、ダウンタイム算出機能１５２は、これと同時に、部品稼働データベース１５４が保持する、各鉱山機械１０の負荷率（鉱山機械１０に取り付けられた各種センサ１１から得ているセンサデータ）の記録から、使用部品のダウンタイムを算出する。この算出手法の詳細は後述する。 In addition, when the countermeasure extraction unit 130 recognizes the necessity of component replacement as a countermeasure to be taken, and when the used component is specified in the plan creation unit 140 regarding the component replacement, the life calculation function 151 in the performance prediction unit 150 is Referring to the work history for each component recorded in the component history database 153, the life history of the used component is calculated. In addition, the life calculation function 151 transmits information on the calculated life record to the output management unit 160. The output management unit 160 stores and manages this in the prediction result database 162. At this time, the downtime calculation function 152 of the performance prediction unit 150 identifies a part whose status has been exchanged in the part history database 153, that is, a part that has already reached the end of its life. Read from the failure history database 124 and calculate the downtime based on the failure history. Details of this calculation method will be described later. At the same time, the downtime calculation function 152 is recorded from the load factor of each mining machine 10 (sensor data obtained from various sensors 11 attached to the mining machine 10) held in the part operation database 154. Calculate the downtime of the parts used. Details of this calculation method will be described later.
　ダウンタイム算出機能１５２は、こうして得られた、故障履歴に基づくダウンタイムと、センサデータに基づくダウンタイムとに差異があるか判定し、その判定結果をクライアント端末１９０に送信してユーザに提示し、ユーザにいずれかのダウンタイムを選択させても良い。あるいは、ダウンタイム算出機能１５２は、クライアント端末１９０を介して、いずれかのダウンタイムの修正をユーザから受け付けても良い。性能予測部１５０は、こうして一意に定めたダウンタイムについて、出力管理部１６０に送信する。出力管理部１６０は、これを予測結果データベース１６２に格納し管理する。なお、ダウンタイムの修正に関しては、鉱山機械１０における異常発生時のみならず、平時であっても、ダウンタイム算出機能１５２がユーザからの指示を受けて実行するとしても良い。 The downtime calculation function 152 determines whether there is a difference between the downtime based on the failure history thus obtained and the downtime based on the sensor data, and transmits the determination result to the client terminal 190 to present to the user. The user may be allowed to select any downtime. Alternatively, the downtime calculation function 152 may accept any downtime correction from the user via the client terminal 190. The performance prediction unit 150 transmits the downtime uniquely determined in this way to the output management unit 160. The output management unit 160 stores and manages this in the prediction result database 162. In addition, regarding the correction of the down time, the down time calculation function 152 may be executed in response to an instruction from the user not only when an abnormality occurs in the mining machine 10 but also during normal times.
　また、保守計画立案支援システム１００が備える出力管理部１６０は、上述した異常診断部１２０、対策抽出部１３０、および計画作成部１４０が出力した結果を、保守計画データベース１６１に、性能予測部１５０が出力した結果を予測結果データベース１６２に、それぞれ保持し、管理する。また、ネットワーク１８０を介してアクセスしてきたクライアント端末１９０に対し、保守計画データベース１６１や予測結果データベース１６２のデータを出力する。この出力に当たっては、クライアント端末１９０からのユーザ要求に応じて、クライアント端末１９０に出力する内容を変更しても良い。 The output management unit 160 included in the maintenance plan planning support system 100 also outputs the results output from the above-described abnormality diagnosis unit 120, countermeasure extraction unit 130, and plan creation unit 140 to the maintenance plan database 161 and the performance prediction unit 150. The output results are held and managed in the prediction result database 162, respectively. Further, the data of the maintenance plan database 161 and the prediction result database 162 is output to the client terminal 190 accessed via the network 180. In this output, the content output to the client terminal 190 may be changed in response to a user request from the client terminal 190.
　続いて、本実施形態の保守計画立案支援システム１００が用いるデータベース類について説明する。図２Ａに、本実施形態における現象履歴データベース１２３の一例を示す。現象履歴データベース１２３は、過去に、鉱山機械１０について観測された現象の履歴を蓄積したデータベースである。そのデータ構造は、現象ＩＤ２０１をキーとして、発生日時２０２と、サイトＩＤ２０３と、機械ＩＤ２０４と、型名２０５と、現象コード２０６と、現象内容２０７と、部位コード２０８と、部位名２０９と、ｎ個のセンサデータ２１０から成るレコードの集合体である。 Subsequently, databases used by the maintenance plan planning support system 100 of this embodiment will be described. FIG. 2A shows an example of the phenomenon history database 123 in the present embodiment. The phenomenon history database 123 is a database in which the history of phenomena observed for the mining machine 10 in the past is accumulated. The data structure includes an occurrence date and time 202, a site ID 203, a machine ID 204, a model name 205, a phenomenon code 206, a phenomenon content 207, a part code 208, a part name 209, n, using the phenomenon ID 201 as a key. This is a set of records composed of sensor data 210.
　上述の現象ＩＤ２０１には、鉱山機械１０について過去に観測された現象を一意に特定するためのＩＤが格納されている。また、発生日時２０２には、該当現象が生じた日時が格納されている。また、サイトＩＤ２０３には、該当現象が観測された鉱山機械１０が運用されていたサイトすなわち鉱山のＩＤが格納されている。また、機械ＩＤ２０４には、該当現象が観測された鉱山機械１０のＩＤが格納されている。また、型名２０５には、鉱山機械１０の型名が格納されている。また、現象コード２０６には、該当現象を示すコードが格納されており、そのコードに対応する現象の内容が現象内容２０７に格納されている。また、部位コード２０８には、該当現象が観測された鉱山機械１０の部位を示すコードが格納されており、部位名２０９にはその部位の名称が格納されている。なお、上述した各コードとコードの示す内容や名称については、別途マスタテーブルを作成し、管理しても良い。また、ｎ個のセンサデータ２１０には、各センサ１１の観測した情報が格納されている。センサデータ２１０に格納される情報は、異常の有無を示すものであっても良いし、センサ１１が観測した値そのものであっても良い。なお、この現象履歴データベース１２３において、上述のセンサデータ２１０に加えて、１または複数のセンサデータから監視システム１７０が算出した、異常検出用のパラメータを格納するとしても良い。 In the phenomenon ID 201 described above, an ID for uniquely identifying a phenomenon observed in the past for the mining machine 10 is stored. The occurrence date 202 stores the date when the corresponding phenomenon occurred. Further, the site ID 203 stores the ID of the site where the mining machine 10 where the corresponding phenomenon is observed, that is, the mine. The machine ID 204 stores the ID of the mining machine 10 in which the corresponding phenomenon is observed. The model name 205 stores the model name of the mining machine 10. The phenomenon code 206 stores a code indicating the corresponding phenomenon, and the phenomenon content corresponding to the code is stored in the phenomenon content 207. Further, the part code 208 stores a code indicating the part of the mining machine 10 where the phenomenon is observed, and the part name 209 stores the name of the part. In addition, about each code | cord | chord mentioned above, and the content and name which a code | cord | chord shows, you may create and manage a master table separately. Further, n sensor data 210 stores information observed by each sensor 11. The information stored in the sensor data 210 may indicate the presence or absence of an abnormality, or may be a value itself observed by the sensor 11. In this phenomenon history database 123, in addition to the sensor data 210 described above, an abnormality detection parameter calculated by the monitoring system 170 from one or a plurality of sensor data may be stored.
　図２Ｂに故障履歴データベース１２４の一例を示す。故障履歴データベース１２４は、鉱山機械１０において過去に生じた故障の履歴を蓄積したデータベースである。この故障履歴データベース１２４は、故障ＩＤ２１１をキーとして、現象ＩＤ２１２と、発生日時２１３と、機械ＩＤ２１４と、型名２１５と、部品シリアル番号２１６と、部位コード２１７と、部位名２１８と、原因部品番号２１９と、原因部品再生品判定２２０と、アワメータ２２１と、故障コード２２２と、故障内容２２３から成るレコードの集合体である。 FIG. 2B shows an example of the failure history database 124. The failure history database 124 is a database in which a history of failures that have occurred in the past in the mining machine 10 is accumulated. This failure history database 124 uses a failure ID 211 as a key, a phenomenon ID 212, an occurrence date and time 213, a machine ID 214, a model name 215, a part serial number 216, a part code 217, a part name 218, and a cause part number. 219, a cause component recycled product determination 220, an hour meter 221, a failure code 222, and a failure content 223.
　上述の故障ＩＤ２１１には、鉱山機械１０において過去に生じた故障を一意に特定するためのＩＤが格納されている。また、現象ＩＤ２１２には、該当故障に先立って鉱山機械１０にて観測された現象を特定するＩＤが格納されている。この現象ＩＤ２１２は、上述した現象履歴データベース１２３における現象ＩＤ２０１と共通する。また、発生日時２１３には、該当故障が発生した日時が格納されている。また、機械ＩＤ２１４には、該当故障が発生した鉱山機械１０を特定するＩＤが格納されており、型名２１５には該当鉱山機械１０の型名が格納されている。また、部品シリアル番号２１６のは、該当鉱山機械１０において故障した部品を一意に特定するための番号が格納されている。また、部位コード２１７及び部位名２１８には、故障した鉱山機械１０の部位を示すコードと、その名称が格納されている。また、原因部品番号２１９には、故障した部品の製品番号が格納されている。また、原因部品再生品判定２２０には、故障した部品が再生部品であるか新品であるかを判定するフラグが格納されている。また、アワメータ２２１には、鉱山機械１０に備わる稼働時間計測用のアワメータの指示値が格納されている。このアワメータの指示値は、該当部品が故障した時点での指示値となる。また、故障コード２２２には、発生した故障の内容を示すコードが格納されており、故障内容２２３には、発生した故障の内容が格納されている。なお、上述した故障履歴データベース１２４における、各コードと内容の関係も、上述した現象履歴データベース１２３における現象や部位と同様に、別途マスタテーブルを作成して管理することもできる。 In the above-described failure ID 211, an ID for uniquely identifying a failure that has occurred in the past in the mining machine 10 is stored. The phenomenon ID 212 stores an ID for identifying a phenomenon observed in the mining machine 10 prior to the failure. This phenomenon ID 212 is common to the phenomenon ID 201 in the phenomenon history database 123 described above. The occurrence date and time 213 stores the date and time when the corresponding failure occurred. The machine ID 214 stores an ID for identifying the mining machine 10 in which the corresponding failure has occurred, and the model name 215 stores the model name of the corresponding mining machine 10. In addition, the part serial number 216 stores a number for uniquely identifying a failed part in the mining machine 10. The part code 217 and the part name 218 store a code indicating the part of the failed mining machine 10 and its name. The cause part number 219 stores the product number of the failed part. The cause component recycled product determination 220 stores a flag for determining whether the failed component is a recycled component or a new one. Further, the hour meter 221 stores an instruction value of an hour meter for operating time measurement provided in the mining machine 10. The indication value of this hour meter becomes the indication value at the time when the corresponding part fails. The failure code 222 stores a code indicating the content of the failure that has occurred, and the failure content 223 stores the content of the failure that has occurred. Note that the relationship between each code and content in the failure history database 124 described above can also be managed by creating a separate master table in the same manner as the phenomenon and location in the phenomenon history database 123 described above.
　図３に、本実施形態の作業履歴データベース１３２の一例を示す。作業履歴データベース１３２は、鉱山機械１０において過去に発生した故障に対し行われた保守作業の履歴を蓄積したデータベースである。この作業履歴データベース１３２は、作業ＩＤ３０１をキーとして、故障ＩＤ３０２と、対応開始日時３０３と、対応終了日時３０４と、機械ＩＤ３０５と、故障コード３０６と、故障内容３０７と、部位コード３０８と、部位名３０９と、原因部品シリアル番号３１０と、原因部品番号３１１と、作業コード３１２と、作業内容３１３と、交換部品シリアル番号３１４と、交換部品番号３１５と、交換部品再生品判定３１６から構成されるレコードの集合体となっている。そのうち故障ＩＤ３０２は、故障履歴データベース１２４と共通する。 FIG. 3 shows an example of the work history database 132 of the present embodiment. The work history database 132 is a database that accumulates a history of maintenance work performed for failures that have occurred in the past in the mining machine 10. The work history database 132 uses the work ID 301 as a key, a failure ID 302, a corresponding start date / time 303, a corresponding end date / time 304, a machine ID 305, a fault code 306, a fault content 307, a part code 308, and a part name. 309, cause part serial number 310, cause part number 311, work code 312, work content 313, replacement part serial number 314, replacement part number 315, and replacement part recycled product determination 316. It is an aggregate of. Of these, the failure ID 302 is common to the failure history database 124.
　上述の作業ＩＤ３０１には、故障ＩＤ３０２に対応した故障に対して実施された作業を特定するＩＤが格納されている。また、対応開始日時３０３及び対応終了日時３０４には、該当作業が開始された時間と該当作業が終了した時間が格納されている。また、機械ＩＤ３０５には、故障が発生し保守作業を実施した鉱山機械１０を特定するＩＤが格納されている。また、故障コード３０６及び故障内容３０７には、発生した故障を示すコードとその内容が格納されている。また、部位コード３０８及び部位名３０９には、故障が発生した部位を示すコードとその名称が格納されている。また、原因部品シリアル番号３１０には、故障した部品を一意に特定する番号が格納され、原因部品番号３１１にはその製品番号が格納されている。また、作業コード３１２には、実施した保守作業の内容に対応したコードが格納されており、作業内容３１３にはその保守作業の内容が格納されている。該当保守作業が部品交換を伴うものであった場合、故障した部品に代えて新たに鉱山機械１０に取り付けることになった部品のＩＤが、交換部品シリアル番号３１４に格納されている。また、交換部品番号３１５に、その交換した部品の製品番号が格納され、交換部品再生品判定３１６に、その部品が再生部品であるか否かを示すフラグが格納されている。 In the above-described work ID 301, an ID for identifying the work performed for the failure corresponding to the failure ID 302 is stored. Also, the corresponding start date and time 303 and the corresponding end date and time 304 store the time when the corresponding work was started and the time when the corresponding work was completed. Further, the machine ID 305 stores an ID for identifying the mining machine 10 in which a failure has occurred and the maintenance work has been performed. The failure code 306 and the failure content 307 store a code indicating the failure that has occurred and its content. In the part code 308 and the part name 309, a code indicating the part where the failure has occurred and its name are stored. The cause part serial number 310 stores a number that uniquely identifies the failed part, and the cause part number 311 stores the product number. The work code 312 stores a code corresponding to the content of the maintenance work performed, and the work content 313 stores the content of the maintenance work. If the corresponding maintenance work involves parts replacement, the ID of the part to be newly attached to the mining machine 10 instead of the failed part is stored in the replacement part serial number 314. The replacement part number 315 stores the product number of the replaced part, and the replacement part recycled product determination 316 stores a flag indicating whether or not the part is a recycled part.
　以上、図２Ａ及び図３に一例を示した各履歴データベースは、本発明における第１データベースにあたり、保守業務実施者によって作成、管理される性質のものであり、保守業務を実施することで継続して拡充される。 As described above, each history database shown in FIG. 2A and FIG. 3 is the first database in the present invention, and is created and managed by a maintenance business operator, and is continued by performing the maintenance business. Expanded.
　図４に本実施形態における、保守作業データベース１３３の一例を示す。保守作業データベース１３３は、作業機械別に規定された標準の保守作業の情報を保持する本発明における第２データベースに該当する。この保守作業データベース１３３は、鉱山機械１０の型ごとに、実施される標準的な保守作業の必要資源やコストの情報を格納したデータベースである。保守作業データベース１３３は、型名４０１をキーとして、作業コード４０２と、作業内容４０３と、部位コード４０４と、部位名４０５と、部品番号４０６と、交換部品番号４０７と、作業費用４０８と、標準作業時間４０９と、必要機材４１０と、必要保守員技能４１１から構成されるレコードの集合体となっている。 FIG. 4 shows an example of the maintenance work database 133 in this embodiment. The maintenance work database 133 corresponds to the second database in the present invention that holds information on standard maintenance work defined for each work machine. This maintenance work database 133 is a database that stores information on necessary resources and costs of standard maintenance work to be performed for each type of mining machine 10. The maintenance work database 133 uses the model name 401 as a key, the work code 402, the work content 403, the part code 404, the part name 405, the part number 406, the replacement part number 407, the work cost 408, and the standard. This is a set of records composed of a work time 409, necessary equipment 410, and necessary maintenance staff skills 411.
　上述の型名４０１は、保守作業を実施する対象の鉱山機械１０の型名が格納されている。また、作業コード４０２と、作業内容４０３には、それぞれ作業内容を特定するコードと、その内容が格納されている。また、部位コード４０４と、部位名４０５と、部品番号４０６には、鉱山機械１０において保守作業を実施する対象部位を示すコードと、その名称と、対象部品の製品番号がそれぞれ格納されている。なお、作業コード４０２は、同じ作業内容であっても、鉱山機械１０の種別や部位、部品の差異によって、費用や作業時間、必要となる資源が異なる場合は、異なるコードとなっている。また、交換部品番号４０７には、該当保守作業において部品交換を伴う場合の、新たに取付ける部品の製品番号が格納されている。他方、該当保守作業において部品交換を伴わない場合、交換部品番号４０７は空白となっているか、或いは所定の判定記号が格納されている。また、作業費用４０８と、標準作業時間４０９と、必要機材４１０と、必要保守員技能４１１には、該当保守作業に要する費用と、保守作業に要する時間と、保守作業に要する機材名と、保守作業に要する保守員の技能名がそれぞれ格納されている。 The model name 401 described above stores the model name of the mining machine 10 to be subjected to maintenance work. Further, the work code 402 and the work content 403 respectively store a code for specifying the work content and the content thereof. Further, the part code 404, the part name 405, and the part number 406 respectively store a code indicating a target part where maintenance work is performed in the mining machine 10, a name thereof, and a product number of the target part. Even if the work code 402 has the same work content, the work code 402 is a different code when the cost, work time, and required resources differ depending on the type, part, and part of the mining machine 10. In addition, the replacement part number 407 stores the product number of a part to be newly attached when the part is replaced in the corresponding maintenance work. On the other hand, if no part replacement is involved in the maintenance work, the replacement part number 407 is blank or a predetermined determination symbol is stored. In addition, the work cost 408, the standard work time 409, the necessary equipment 410, and the necessary maintenance staff skill 411 include the cost required for the maintenance work, the time required for the maintenance work, the name of the equipment required for the maintenance work, and the maintenance. Stores the skill names of maintenance personnel required for the work.
　図５に本実施形態における日程データベース１４３の一例を示す。日程データベース１４３は、保守作業用の各人員および各機材の稼働可能時期の情報を保持する本発明における第３データベースに該当する。日程データベース１４３は、日付５００と、保守員日程５０１と、機材日程５０２から構成されたレコードの集合体となっている。日程データベース１４３は、保守事業者が保有する機材及び雇用する保守員のスケジュールを格納するデータベースと言える。図５に示す日程データベース１４３の例では、機材及び保守員が保守作業に対応可能な期間を"１"で、対応不可能な期間を"０"で表している。なお、図５に示す日程データベース１４３の例では、日付５００を一日単位で表記しているが、１時間毎や８時間毎、１週間単位など、保守作業の管理者等が任意に設定してよい。 FIG. 5 shows an example of the schedule database 143 in the present embodiment. The schedule database 143 corresponds to a third database in the present invention that holds information on the availability of each person for maintenance work and each equipment. The schedule database 143 is a collection of records including a date 500, a maintenance staff schedule 501, and an equipment schedule 502. The schedule database 143 can be said to be a database that stores equipment owned by a maintenance company and a schedule of maintenance personnel employed. In the example of the schedule database 143 shown in FIG. 5, the period in which the equipment and the maintenance staff can handle the maintenance work is represented by “1”, and the period in which the equipment and maintenance staff cannot handle is represented by “0”. In the example of the schedule database 143 shown in FIG. 5, the date 500 is expressed in units of one day. However, the maintenance manager or the like arbitrarily sets such as every hour, every eight hours, or every week. It's okay.
　図６Ａに本実施形態の部品調達データベース１４４が含む部品在庫テーブル６００の一例を示す。なお、部品調達データベース１４４は、部品在庫テーブル６００と、輸送手段テーブル６１０と、互換部品テーブル６２０から構成され、保守作業に用いられる部品ないしその旧版部品または再生部品の各在庫と価格、および、輸送先別および輸送手段別の納期および輸送コストの情報とを、倉庫別に保持する、本発明における第４データベースに該当する。 FIG. 6A shows an example of a parts inventory table 600 included in the parts procurement database 144 of this embodiment. The parts procurement database 144 is composed of a parts inventory table 600, a transport means table 610, and a compatible parts table 620, and each stock and price of parts used for maintenance work or old version parts or recycled parts, and transportation. This corresponds to the fourth database according to the present invention in which the delivery date and transportation cost information for each destination and transportation means are stored for each warehouse.
　部品在庫テーブル６００は、部位コード６０１と、部位名６０２と、部品番号６０３と、再生品判定６０４と、倉庫６０５と、在庫６０６と、価格６０７から成るレコードの集合体となっている。各レコードは、どの部品がどの倉庫にどれだけ在庫されており、価格はいくらであるかを示している。部位コード６０１と、部位名６０２と、部品番号６０３と、再生品判定６０４は、在庫部品の該当属性の情報である。鉱山機械１０の保守に用いる交換用の部品には、既に上述したように、最新版である新品の部品、遡及対策が未実施である古いバージョンの旧版部品、および、故障した部品を修理、再生した再生部品がある。このうち再生部品は、その消耗度によってランク分けされている。ここでは、新品を"Ｎ"、再生部品を、消耗度が低いもの、すなわちランクの高い物から"Ｒｅ－Ａ"、"Ｒｅ－Ｂ"、"Ｒｅ－Ｃ"、と表記している。旧版部品は型番が古いだけで未使用の新品であるから、図６Ａの例では"Ｎ"とされているが、勿論、旧版部品を特定する表記を行ってもよい。また、倉庫６０５には、在庫部品が保管されている倉庫の名称が、在庫６０６には、在庫部品の員数が、価格６０７には、在庫部品の単価がそれぞれ格納されている。在庫６０６及び価格６０７は、時間によって変わる変数であるが、ここでは常に最新の値を格納しているものとする。 The parts inventory table 600 is an aggregate of records including a part code 601, a part name 602, a part number 603, a recycled product determination 604, a warehouse 605, a stock 606, and a price 607. Each record shows how many parts are stocked in which warehouse and how much the price is. The part code 601, the part name 602, the part number 603, and the recycled product determination 604 are information on the corresponding attribute of the inventory part. As described above, the replacement parts used for the maintenance of the mining machine 10 are repaired and refurbished as new parts that are the latest version, old version parts that have not been retroactively implemented, and failed parts. There are recycled parts. Among these, the recycled parts are ranked according to the degree of wear. Here, “N” indicates a new article, and “Re-A”, “Re-B”, and “Re-C” indicate that a recycled part has a low level of wear, that is, a high-ranked one. Since the old version part is an old new article with only an old model number, it is “N” in the example of FIG. 6A. Of course, a notation for identifying the old version part may be used. The warehouse 605 stores the name of the warehouse where the stock parts are stored, the stock 606 stores the number of stock parts, and the price 607 stores the unit price of the stock parts. The stock 606 and the price 607 are variables that change with time, but here, it is assumed that the latest values are always stored.
　図６Ｂに、本実施形態の部品調達データベース１４４が含む輸送手段テーブル６１０の一例を示す。部品調達データベース１４４が含む輸送手段テーブル６１０は、倉庫６１１と、輸送先６１２と、輸送手段６１３と、納期６１４と、輸送費６１５から構成されるレコードの集合体となっている。このレコードは、輸送元たる倉庫から輸送先たるサイトまで、どのような輸送手段があり、その所要時間と費用はいくらになるかを示したものとなる。そのうち、倉庫６１１には、部品の輸送元となる倉庫の名称が格納されている。また、輸送先６１２には、部品の輸送先となるサイト名が格納されている。また、輸送手段６１３には、部品輸送に用いる手段の名称が格納されている。また、納期６１４と、輸送費６１５には、各ケースにおける納期と、その費用がそれぞれ格納されている。 FIG. 6B shows an example of the transportation means table 610 included in the parts procurement database 144 of this embodiment. The transportation means table 610 included in the parts procurement database 144 is a collection of records including a warehouse 611, a transportation destination 612, a transportation means 613, a delivery date 614, and a transportation cost 615. This record shows what kind of transportation is available from the warehouse as the transportation source to the site as the transportation destination, and how much time and cost it takes. Of these, the warehouse 611 stores the name of the warehouse that is the transportation source of the parts. In addition, in the transportation destination 612, a site name that is a transportation destination of the parts is stored. The transportation means 613 stores names of means used for parts transportation. In addition, the delivery date and the cost in each case are stored in the delivery date 614 and the transportation cost 615, respectively.
　図６Ｃに、本実施形態の部品調達データベース１４４が含む互換部品テーブル６２０の一例を示す。部品調達データベース１４４が含む互換部品テーブル６２０は、型名６２１と、部位コード６２２と、部位名６２３と、部品番号６２４と、定期交換間隔６２５から成るレコードの集合体となっている。このレコードは、ある鉱山機械１０の、ある部位に使用可能な互換部品の一覧とその定期交換間隔を示している。このうち、型名６２１には、対象とする鉱山機械１０の型名を示す情報が、部位コード６２２には、該当鉱山機械１０における対象部位を特定する情報が、部位名６２３には、部位コード６２２が示す部位の名称が、それぞれ格納されている。これらの、型名６２１、部位コード６２２、部位名６２３の各情報によって、対象とする鉱山機械１０とその部位を特定できる。また、部品番号６２４には、対象とする鉱山機械１０の部位に取付けて使用可能な部品の製品番号が格納されている。型名６２１、部位コード６２２、部位名６２３の各情報によって特定される、或る鉱山機械１０の所定部位に適用できる部品らとしては、仕様や機能がほぼ同一である、型番が最新の新品部品、その旧版部品、再生部品、の３種類の部品が含まれている。また、定期交換間隔６２５には、部品メーカーが推奨する部品交換間隔が格納されている。 FIG. 6C shows an example of the compatible component table 620 included in the component procurement database 144 of this embodiment. The compatible parts table 620 included in the parts procurement database 144 is a collection of records including a model name 621, a part code 622, a part name 623, a part number 624, and a regular replacement interval 625. This record shows a list of compatible parts that can be used in a certain part of a certain mining machine 10 and its regular replacement interval. Among these, the model name 621 includes information indicating the model name of the target mining machine 10, the part code 622 includes information specifying the target part of the mining machine 10, and the part name 623 includes the part code. The names of the parts indicated by 622 are respectively stored. The target mining machine 10 and its part can be specified by each information of the model name 621, the part code 622, and the part name 623. The part number 624 stores a product number of a part that can be used by being attached to the target mining machine 10. The parts that are specified by the information of the type name 621, the part code 622, and the part name 623 and that can be applied to a predetermined part of a certain mining machine 10 have the same specifications and functions, and are new parts with the latest model numbers. 3 parts of the old version parts and the recycled parts are included. The periodic replacement interval 625 stores a component replacement interval recommended by a component manufacturer.
　図７Ａに本実施形態の顧客知識データベース１４５が含む、運用ロステーブル７００の一例を示す。顧客知識データベース１４５は、各鉱山機械１０の、故障時における負荷率別の残寿命および負荷率低下に伴う鉱山機械１０の使用者における経済損失の情報とを対応づけて保持する、本発明における第５データベースに該当する。なお、顧客知識データベース１４５は、運用ロステーブル７００と、残寿命テーブル７１０と、顧客テーブル７３０で構成される。顧客とは、鉱山機械１０の使用者であり、鉱山機械１０の保守サービスの顧客である。 FIG. 7A shows an example of the operation loss table 700 included in the customer knowledge database 145 of this embodiment. The customer knowledge database 145 stores the remaining life of each mining machine 10 according to the load factor at the time of failure and information on the economic loss in the user of the mining machine 10 due to the load factor reduction in association with each other. Corresponds to 5 databases. The customer knowledge database 145 includes an operation loss table 700, a remaining life table 710, and a customer table 730. The customer is a user of the mining machine 10 and a customer of the maintenance service of the mining machine 10.
　運用ロステーブル７００は、顧客ＩＤ７０１と、サイトＩＤ７０２と、型名７０３と、部位コード７０４と、部位名７０５と、負荷率７０６と、運用ロス７０７から成るレコードの集合体となっている。運用ロステーブル７００は、ある顧客の、あるサイトにおいて、鉱山機械１０における或る部位の負荷率を制限した場合に生じる、顧客の経済損失を示している。 The operation loss table 700 is a collection of records including a customer ID 701, a site ID 702, a model name 703, a part code 704, a part name 705, a load factor 706, and an operation loss 707. The operation loss table 700 shows the customer's economic loss that occurs when the load factor of a certain part of the mining machine 10 is limited at a certain customer's site.
　顧客ＩＤ７０１には、顧客を特定するＩＤを、サイトＩＤ７０２には、該当鉱山機械１０が運用されているサイトを特定するＩＤが格納されている。型名７０３と、部位コード７０４と、部位名７０５には、鉱山機械１０の型名と、その部位を示すコードと、部位の名称が格納されている。また、負荷率７０６には、鉱山機械１０における通常の定格稼働における負荷を１００とした場合の負荷制限率が格納されている。ここで「負荷」とは、例えば「部位」がモータであれば、その回転数やトルクに該当する。また、運用ロス７０７には、負荷に制限を課した場合に生じる、鉱山機械１０を運用する顧客の単位時間あたりの経済損失の情報が格納されている。この運用ロス７０７は、顧客の運用方針や、サイトで採掘する資源の種別、負荷を制限する部位によって特徴づけられるものとなる。 The customer ID 701 stores an ID for identifying a customer, and the site ID 702 stores an ID for identifying a site where the corresponding mining machine 10 is operated. In the type name 703, the part code 704, and the part name 705, the type name of the mining machine 10, the code indicating the part, and the name of the part are stored. Further, the load factor 706 stores a load limiting rate when the load at the normal rated operation in the mining machine 10 is 100. Here, the “load” corresponds to the number of rotations and torque when the “part” is a motor, for example. The operation loss 707 stores information on economic loss per unit time of a customer who operates the mining machine 10 that occurs when a load is limited. This operation loss 707 is characterized by the customer's operation policy, the type of resource mined at the site, and the part that limits the load.
　図７Ｂに、本実施形態の顧客知識データベース１４５が含む、残寿命テーブル７１０の一例を示す。残寿命テーブル７１０は、顧客ＩＤ７１１と、サイトＩＤ７１２と、型名７１３と、部位コード７１４と、部位名７１５と、現象コード７１６と、現象内容７１７と、故障コード７１８と、故障内容７１９と、負荷率７２０と、残寿命７２１から構成されるレコードの集合体となっている。 FIG. 7B shows an example of the remaining life table 710 included in the customer knowledge database 145 of this embodiment. The remaining life table 710 includes customer ID 711, site ID 712, model name 713, part code 714, part name 715, phenomenon code 716, phenomenon content 717, failure code 718, failure content 719, load This is a set of records composed of a rate 720 and a remaining life 721.
　このうち、顧客ＩＤ７１１には、顧客を特定するＩＤが格納され、サイトＩＤには、サイトを特定するＩＤが格納されている。また、型名７１３と、部位コード７１４と、部位名７１５には、対象とする鉱山機械１０の型名と、部位を示すコードと、該当部位の名称が格納されている。また、現象コード７１６と、現象内容７１７には、該当部位に生じた現象を示すコードと、それに対応する現象の内容が格納されている。また、故障コード７１８と、故障内容７１９には、現象と対応する故障を示すコードと、その故障の内容が格納されている。また、負荷率７２０には、型名７１３、部位コード７１４、部位名７１５で特定できる鉱山機械１０の該当部位における負荷率が格納されている。また、残寿命７２１には、該当部位にて異常を検知してから故障が発生するまでの時間が格納されている。　残寿命７２１は、サイトＩＤ７１２で指定されるサイトにおいて、顧客ＩＤ７１１で指定される顧客が運用する、型名７１３の鉱山機械１０の、部位コード７１４、部位名７１５で指定する部位にて、現象コード７１６及び現象内容７１７の現象が生じたとき、負荷率７２０の負荷率で該当部位を稼働させると、どのくらいの時間で、故障コード７１８及び故障内容７１９に格納した故障に至るかを意味している。 Among these, the customer ID 711 stores an ID for specifying a customer, and the site ID stores an ID for specifying a site. The model name 713, the part code 714, and the part name 715 store the model name of the target mining machine 10, the code indicating the part, and the name of the corresponding part. The phenomenon code 716 and the phenomenon content 717 store a code indicating a phenomenon that has occurred in the corresponding part and the content of the corresponding phenomenon. The failure code 718 and the failure content 719 store a code indicating the failure corresponding to the phenomenon and the content of the failure. Also, the load factor 720 stores the load factor at the corresponding part of the mining machine 10 that can be specified by the model name 713, the part code 714, and the part name 715. Further, the remaining life 721 stores a time from when an abnormality is detected at a corresponding part until a failure occurs. The remaining life 721 is the phenomenon code at the site specified by the site code 714 and the site name 715 of the mining machine 10 of the model name 713 operated by the customer specified by the customer ID 711 at the site specified by the site ID 712. 716 and the phenomenon content 717, when the corresponding part is operated at the load factor of 720, it means how long the failure is stored in the failure code 718 and the failure content 719. .
　図７Ｃに、本実施形態の顧客知識データベース１４５が含む、顧客テーブル７３０の一例を示す。顧客テーブル７３０は、顧客ＩＤ７３１と、顧客名７３２と、サイトＩＤ７３３と、サイト名７３４と、サイト種別７３５と、国名コード７３６から構成されるレコードの集合体となっている。そのうち顧客ＩＤ７３１には、顧客を特定するＩＤが、顧客名７３２にはその顧客の名称が、サイトＩＤ７３３には、該当顧客が運営するサイトを特定するＩＤが、サイト名７３４には、そのサイトの名称が、サイト種別７３５には、そのサイトで採掘される資源の種別が、国名コード７３６は、サイトが存在する国を表す国名コードがそれぞれ格納されている。上述した運用ロステーブル７００と、残寿命テーブル７１０と、顧客テーブル７３０は、顧客ＩＤと、サイトＩＤをキーとして互いのレコードが関連付けられる。 FIG. 7C shows an example of the customer table 730 included in the customer knowledge database 145 of this embodiment. The customer table 730 is an aggregate of records including a customer ID 731, a customer name 732, a site ID 733, a site name 734, a site type 735, and a country name code 736. Among them, the customer ID 731 has an ID for identifying the customer, the customer name 732 has the name of the customer, the site ID 733 has an ID for identifying the site operated by the customer, and the site name 734 has the ID of the site. The name of the site type 735 stores the type of resource mined at the site, and the country name code 736 stores a country name code representing the country in which the site exists. The operation loss table 700, the remaining life table 710, and the customer table 730 described above are associated with each other using the customer ID and the site ID as keys.
　図８に本実施例の定期保守データベース１４６の一例を示す。定期保守データベース１４６は、保守事業者が鉱山機械１０に関して計画する定期保守のスケジュールを格納する本発明の第９データベースに該当する。図８に例示する定期保守データベース１４６では、定期保守が計画されている日を'１'で表し、定期保守実施の予定がない日を'０'で表している。また、図５に例示した日程データベース１４３と同様に、日程の単位はユーザが任意に設定してもよい。 FIG. 8 shows an example of the regular maintenance database 146 of this embodiment. The regular maintenance database 146 corresponds to the ninth database of the present invention that stores a schedule of regular maintenance planned by the maintenance company for the mining machine 10. In the periodic maintenance database 146 illustrated in FIG. 8, the date on which the scheduled maintenance is planned is represented by “1”, and the date on which the scheduled maintenance is not scheduled to be performed is represented by “0”. Further, similarly to the schedule database 143 illustrated in FIG. 5, the schedule unit may be arbitrarily set by the user.
　図９に本実施例の部品履歴データベース１５３の一例を示す。部品履歴データベース１５３は、個々の部品ないしその旧版部品または再生部品の、鉱山機械１０への取付及び鉱山機械１０からの取外に関する情報を格納した本発明の第６データベースに該当する。この部品履歴データベース１５３は、部品シリアル番号９０１と、部位コード９０２と、部位名９０３と、部品番号９０４と、再生品判定９０５と、顧客ＩＤ９０６と、サイトＩＤ９０７と、取付機械ＩＤ９０８と、状況フラグ９０９と、取付日時９１０と、取外日時９１１から構成されるレコードの集合体となっている。 FIG. 9 shows an example of the component history database 153 of this embodiment. The parts history database 153 corresponds to a sixth database of the present invention that stores information on the attachment and removal of individual parts or their old version parts or recycled parts from the mining machine 10. The component history database 153 includes a component serial number 901, a part code 902, a part name 903, a part number 904, a remanufactured product determination 905, a customer ID 906, a site ID 907, an attachment machine ID 908, and a status flag 909. And a collection of records composed of an attachment date 910 and a removal date 911.
　そのうち、部品シリアル番号９０１には、保守事業者が保守を実施する部品を一意に特定する番号が格納されている。また、部位コード９０２と部位名９０３は、該当部品が取付けられる部位を示すコードと、その名称が格納されている。また、部品番号９０４と、再生品判定９０５は、上述した部品シリアル番号９０１で指定される部品の製品番号と、再生品判定フラグがそれぞれ格納されている。部品は、取付→取外→再生→取付のサイクルを経て、再生部品として市場に出回る。こうした過程で、再生部品は、前述のように消耗の度合いによってランク付けされている。再生部品は、再生を繰り返すにつれ、再生品判定９０５のランクが下がっていく傾向にある。 Of these, the part serial number 901 stores a number that uniquely identifies the part that the maintenance company performs maintenance on. The part code 902 and the part name 903 store a code indicating the part to which the corresponding part is attached and its name. The part number 904 and the recycled product determination 905 store the product number of the component specified by the component serial number 901 and the recycled product determination flag, respectively. Parts are put on the market as recycled parts through a cycle of mounting → removal → recycling → mounting. In this process, the recycled parts are ranked according to the degree of wear as described above. Recycled parts tend to lower the rank of the recycled product determination 905 as the reproduction is repeated.
　ある部品の取付→取外→再生→取付→…のサイクルを例として示したものが、図９の部品シリアル番号"１００１００－１０３"、"１００１００－１５１"、"１００１００－１８２"、"１００１００－２１３"の各レコードである。これらレコードの関係は、同一個体の部品が再生され繰り返し使用されている様子を示している。 An example of a cycle of mounting a certain part → removing → reproducing → mounting →... Is a part serial number “100100-103”, “100100-151”, “100100-182”, “100100-” in FIG. Each record of 213 ". The relationship between these records shows that parts of the same individual are regenerated and used repeatedly.
　本実施形態においては、部品の取付から取外までを寿命と呼ぶ。この寿命は、同一個体の部品であっても、再生機会ごとに区別するために、それぞれ異なる部品シリアル番号９０１を付与して管理している。また、取付機械ＩＤ９０９には、該当部品が取付けられた鉱山機械１０のＩＤが格納されている。また、顧客ＩＤ９０７と、サイトＩＤ９０８には、その鉱山機械１０を運用する顧客のＩＤと、その鉱山機械１０が稼働するサイトのＩＤがそれぞれ格納されている。また、状況フラグ９１０には、その部品の現在のステータスが格納されている。また、取付日時９１１と取外日時９１２には、該当部品が鉱山機械１０に取付けられた日時と、鉱山機械１０から取り外された日時が格納されている。 In the present embodiment, the life from the attachment to the removal of the parts is called the life. The lifespan is managed by assigning different parts serial numbers 901 in order to distinguish the parts of the same individual for each reproduction opportunity. The mounting machine ID 909 stores the ID of the mining machine 10 to which the corresponding part is attached. Further, the customer ID 907 and the site ID 908 respectively store the ID of the customer who operates the mining machine 10 and the ID of the site where the mining machine 10 operates. The status flag 910 stores the current status of the part. In addition, the installation date / time 911 and the removal date / time 912 store the date / time when the corresponding part was attached to the mining machine 10 and the date / time when the part was removed from the mining machine 10.
　図１０Ａに本実施形態の部品稼働データベース１５４が含む、稼働判定テーブル１０００の一例を示す。なお、部品稼働データベース１５４は部品に取り付けたセンサ１１から取得できる値を用いて部品の稼働／停止の判定を行う情報を格納したものであり、鉱山機械１０の或る部位に取り付けられた部品が稼働しているか否か判定するための判定式を格納した稼働判定テーブル１０００と、ユーザが指定した期間の鉱山機械１０に関するセンサデータを格納した稼働実績テーブル１０１０から構成される。また、本実施形態の部品稼働データベース１５４は、部品履歴データベース１５３（図９）の部品シリアル番号９０１＝"１００１００－１０３"で指定されるモータに関するものとなっている。 FIG. 10A shows an example of an operation determination table 1000 included in the component operation database 154 of this embodiment. The part operation database 154 stores information for determining whether to operate / stop a part using a value acquired from the sensor 11 attached to the part. A part attached to a certain part of the mining machine 10 is stored in the part operation database 154. An operation determination table 1000 that stores a determination formula for determining whether or not it is operating, and an operation result table 1010 that stores sensor data related to the mining machine 10 during a period specified by the user. The component operation database 154 of this embodiment relates to the motor specified by the component serial number 901 = “100100-103” in the component history database 153 (FIG. 9).
　図１０Ａに示す稼働判定テーブル１０００は、型名１００１と、部位コード１００２と、部位名１００３と、判定項目１００４と、判定値１００５と、判定条件１００６の各情報から構成されている。稼働有無の判定対象の部位名１００３が「モータ」である場合を例にとると、判定項目１００４は回転数やトルクといった値になる。図１０Ａの稼働判定テーブル１０００では、例として判定項目１００４の「回転数」が、判定値１００５として「２０００」以上となる期間を「稼働」と判定する条件になっている。 The operation determination table 1000 shown in FIG. 10A includes information of a model name 1001, a part code 1002, a part name 1003, a determination item 1004, a determination value 1005, and a determination condition 1006. Taking as an example the case where the part name 1003 subject to determination of the presence or absence of operation is “motor”, the determination item 1004 is a value such as the rotation speed or torque. In the operation determination table 1000 of FIG. 10A, as an example, a condition in which “period” of the determination item 1004 is “2000” or more as the determination value 1005 is a condition for determining “operation”.
　図１０Ｂに本実施形態の部品稼働データベース１５４が含む、稼働実績テーブル１０１０の一例を示す。稼働実績テーブル１０１０は、部品シリアル番号１０１１と、項目１０１２と、期間１０１３と、平均値１０１４から構成されるレコードの集合体となっている。　そのうち、部品シリアル番号１０１１には、センサ１１が計測を行っている部品を一意に識別する部品番号が格納されている。また、項目１０１２には、該当部品に関して格納しているセンサデータの種別が格納されている。また、期間１０１３には、部品シリアル番号１０１１が示す該当部品に関してセンサ１１が計測を行った期間の情報が格納されている。また、平均値１０１４には、部品シリアル番号１０１１が示す該当部品に関してセンサ１１が計測したセンサデータの、期間１０１３における平均値が格納されている。本実施形態において、期間１０１３は１時間を単位時間としているが、ユーザ操作によって適宜に変更可能としてもよい。 FIG. 10B shows an example of an operation result table 1010 included in the component operation database 154 of the present embodiment. The operation result table 1010 is a collection of records including a component serial number 1011, an item 1012, a period 1013, and an average value 1014. Of these, the part serial number 1011 stores a part number that uniquely identifies the part that the sensor 11 is measuring. The item 1012 stores the type of sensor data stored for the corresponding part. In a period 1013, information on a period during which the sensor 11 has measured the corresponding part indicated by the part serial number 1011 is stored. In addition, the average value 1014 stores an average value of the sensor data measured by the sensor 11 for the corresponding component indicated by the component serial number 1011 in the period 1013. In the present embodiment, the period 1013 is set to one hour as a unit time, but may be appropriately changed by a user operation.
　図１１Ａに本実施形態の保守計画データベース１６１が含む異常診断結果テーブル１１００の一例を示す。なお、保守計画データベース１６１は、異常診断結果テーブル１１００と、対策抽出結果テーブル１１２０と、実施候補テーブル１１３０、調達／運用計画テーブル１１４０から構成される。また、これらテーブル１１００～１１４０らは、異常診断部１２０と、対策抽出部１３０と、計画作成部１４０と、性能予測部１５０の出力として作成される。 FIG. 11A shows an example of the abnormality diagnosis result table 1100 included in the maintenance plan database 161 of this embodiment. The maintenance plan database 161 includes an abnormality diagnosis result table 1100, a countermeasure extraction result table 1120, an execution candidate table 1130, and a procurement / operation plan table 1140. These tables 1100 to 1140 are created as outputs of the abnormality diagnosis unit 120, the countermeasure extraction unit 130, the plan creation unit 140, and the performance prediction unit 150.
　異常診断結果テーブル１１００は、異常診断部１２０が出力するテーブルであり、異常ＩＤ１１０１と、発生日時１１０２と、アワメータ１１０３と、顧客ＩＤ１１０４と、サイトＩＤ１１０５と、機械ＩＤ１１０６と、部品シリアル番号１１０７と、現象コード１１０８と、現象内容１１０９と、故障コード１１１０と、故障内容１１１１から成るレコードの集合体となっている。 The abnormality diagnosis result table 1100 is a table output by the abnormality diagnosis unit 120, and includes an abnormality ID 1101, an occurrence date and time 1102, an hour meter 1103, a customer ID 1104, a site ID 1105, a machine ID 1106, a part serial number 1107, and a phenomenon. This is a set of records including a code 1108, a phenomenon content 1109, a failure code 1110, and a failure content 1111.
　異常ＩＤ１１０１は、上述した異常診断部１２０が特定した異常であり、故障の予兆であると見做した異常を一意に識別するＩＤが格納されている。また、発生日時１１０２及びアワメータ１１０３は、該当異常の検出時の日時及びアワメータの値が格納されている。また、顧客ＩＤ１１０４、サイトＩＤ１１０５、機械ＩＤ１１０６、部品シリアル番号１１０７は、それぞれ異常を検出した鉱山機械１０を保有する顧客を特定するＩＤと、その鉱山機械１０が稼働しているサイトを特定するＩＤと、その鉱山機械１０を特定するＩＤと、異常が検出された部品を特定するＩＤが格納されている。また、現象コード１１０８と現象内容１１０９には、上述した現象診断機能１２１が診断した結果が、故障コード１１１０と故障内容１１１１には、故障診断機能１２２が診断した結果が格納されている。 The anomaly ID 1101 is an anomaly identified by the anomaly diagnosis unit 120 described above, and stores an ID that uniquely identifies an anomaly that is considered to be a sign of failure. The occurrence date and time 1102 and the hour meter 1103 store the date and time and hour value when the corresponding abnormality is detected. The customer ID 1104, the site ID 1105, the machine ID 1106, and the part serial number 1107 are respectively an ID that identifies a customer who owns the mining machine 10 that has detected an abnormality, and an ID that identifies a site where the mining machine 10 is operating. The ID for identifying the mining machine 10 and the ID for identifying the component in which the abnormality is detected are stored. In addition, the result of diagnosis by the phenomenon diagnosis function 121 described above is stored in the phenomenon code 1108 and the phenomenon content 1109, and the result of diagnosis by the failure diagnosis function 122 is stored in the failure code 1110 and the failure content 1111.
　図１１Ｂに本実施形態の保守計画データベース１６１が含む対策抽出結果テーブル１１２０の一例を示す。対策抽出結果テーブル１１２０は、上述した対策抽出部１３０が出力した、対策ＩＤ１１２１と、異常ＩＤ１１２２と、作業コード１１２３と、作業内容１１２４と、標準作業時間１１２５と、作業費用１１２６と、必要機材１１２７と、必要保守員技能１１２８から構成されるレコードの集合体となっている。 FIG. 11B shows an example of the measure extraction result table 1120 included in the maintenance plan database 161 of this embodiment. The countermeasure extraction result table 1120 includes a countermeasure ID 1121, an abnormality ID 1122, a work code 1123, a work content 1124, a standard work time 1125, a work cost 1126, and necessary equipment 1127 output from the above-described countermeasure extraction unit 130. , A set of records composed of necessary maintenance personnel skills 1128.
　そのうち対策ＩＤ１１２１には、上述の異常診断結果テーブル１１００における異常ＩＤ１１０１に対応した異常に対して計画した対策すなわち保守作業を一意に特定するＩＤが格納されている。また、異常ＩＤ１１２２には、上述の異常診断結果テーブル１１００における異常ＩＤ１１０１と共通する異常ＩＤ１１２２が格納されている。また、作業コード１１２３と作業内容１１２４には、作業履歴データベース１３２から抽出された保守作業のコードとその内容が格納されている。また、標準作業時間１１２５と、作業費用１１２６と、必要機材１１２７と、必要保守員技能１１２８には、該当保守作業に関して保守作業データベース１３３から抽出された、保守作業に要する時間と、費用と、機材と、保守員技能の各情報が格納されている。 Among them, the countermeasure ID 1121 stores an ID that uniquely identifies a countermeasure, that is, maintenance work planned for the abnormality corresponding to the abnormality ID 1101 in the abnormality diagnosis result table 1100 described above. The abnormality ID 1122 stores an abnormality ID 1122 that is common to the abnormality ID 1101 in the abnormality diagnosis result table 1100 described above. The work code 1123 and the work content 1124 store the maintenance work code extracted from the work history database 132 and its content. The standard work time 1125, work cost 1126, necessary equipment 1127, and necessary maintenance staff skill 1128 include the time, cost, and equipment required for the maintenance work extracted from the maintenance work database 133 for the relevant maintenance work. And maintenance staff skill information are stored.
　図１１Ｃに本実施形態の保守計画データベース１６１が含む実施候補テーブル１１３０の一例を示す。実施候補テーブル１１３０は、計画作成部１４０の計画機能１４１が、対策抽出部１３０の出力である対策抽出結果テーブル１１２０と日程データベース１４３から抽出し出力したテーブルとなる。この実施候補テーブル１１３０は、日程ＩＤ１１３１、対策ＩＤ１１３２、実施候補１１３３、実施可能日１１３４、対応保守員１１３５、対応機材１１３６、ｔｘ１１３７から構成されるレコードの集合体となっている。 FIG. 11C shows an example of the implementation candidate table 1130 included in the maintenance plan database 161 of this embodiment. The implementation candidate table 1130 is a table that the plan function 141 of the plan creation unit 140 extracts and outputs from the measure extraction result table 1120 and the schedule database 143 that are the outputs of the measure extraction unit 130. This execution candidate table 1130 is an aggregate of records including schedule ID 1131, countermeasure ID 1132, execution candidate 1133, possible date 1134, corresponding maintenance staff 1135, corresponding equipment 1136, and tx1137.
　そのうち日程ＩＤ１１３１には、保守作業を実施する候補期間を特定するＩＤが格納されている。また、対策ＩＤ１１３２には、上述した対策抽出結果テーブル１１２０における対策ＩＤ１１２１と共通し、実施される対策を特定するＩＤが格納されている。また、実施候補１１３３には、日程ＩＤ１１３１に合致した値が格納されている。また、実施日１１３４には、上述した計画作成機能１４１が抽出した、保守作業を実施可能な日程が格納されている。また、保守員１１３５及び機材１１３６には、それぞれ対応に当たる保守員名と機材名が格納されている。また、ｔｘ１１３７には、現時点から保守作業の実施候補日までの猶予時間が格納されている。 Of these, the schedule ID 1131 stores an ID for identifying a candidate period for performing maintenance work. The countermeasure ID 1132 stores an ID that identifies the countermeasure to be implemented, in common with the countermeasure ID 1121 in the countermeasure extraction result table 1120 described above. The execution candidate 1133 stores a value that matches the schedule ID 1131. Further, the execution date 1134 stores a schedule that allows the maintenance work to be extracted, which is extracted by the plan creation function 141 described above. The maintenance staff 1135 and the equipment 1136 store the maintenance staff name and the equipment name corresponding to the correspondence, respectively. In addition, in tx1137, a grace time from the current time to the execution candidate date of maintenance work is stored.
　図１１Ｄに本実施形態の保守計画データベース１６１が含む調達／運用テーブル１１４０の一例を示す。調達／運用テーブル１１４０は、上述した計画作成部１４０の計画作成機能１４１が、在庫部品データベース１４５及び顧客知識データベース１４５を参照して出力した結果が格納されている。図１１Ｄに示す調達／運用テーブル１１４０は、部品交換作業を含む保守作業に関して計画した内容を示すテーブル構成例である。 FIG. 11D shows an example of the procurement / operation table 1140 included in the maintenance plan database 161 of this embodiment. The procurement / operation table 1140 stores the results output by the plan creation function 141 of the plan creation unit 140 described above with reference to the inventory parts database 145 and the customer knowledge database 145. A procurement / operation table 1140 shown in FIG. 11D is a table configuration example showing the contents planned for maintenance work including parts replacement work.
　調達／運用テーブル１１４０は、計画ＩＤ１１４１と、日程ＩＤ１１４２と、部品番号１１４３と、再生品判定１１４４と、倉庫１１４５と、輸送手段１１４６と、納期１１４７と、部品価格１１４８と、輸送費１１４９と、負荷率１１５０と、運用ロス１１５１と、作業ロス１１５２から構成されるレコードの集合体となっている。 The procurement / operation table 1140 includes a plan ID 1141, a schedule ID 1142, a part number 1143, a recycled product determination 1144, a warehouse 1145, a transportation means 1146, a delivery date 1147, a part price 1148, a transportation cost 1149, a load This is a set of records composed of a rate 1150, an operation loss 1151, and a work loss 1152.
　そのうち計画ＩＤ１１４１には、調達／運用計画を一意に特定するためのＩＤが格納されている。なお、保守作業として部品交換を含まず、従って部品調達を要しない計画である場合、この計画ＩＤ１１４１は、運用計画を特定するためのＩＤとなる。 Of these, the plan ID 1141 stores an ID for uniquely identifying the procurement / operation plan. Note that if the maintenance work does not include parts replacement and therefore does not require parts procurement, the plan ID 1141 is an ID for specifying the operation plan.
　また、日程ＩＤ１１４２には、上述した実施候補テーブル１１３０の日程ＩＤ１１３１に対応するＩＤが格納されている。また、部品番号１１４３と再生品判定１１４４には、部品の交換作業で新たに取り付ける部品の部品番号と、該当部品の再生品判定フラグが格納されている。なお、保守作業が部品交換作業を含まない場合、これら部品番号１１４３と再生品判定１１４４は、空白とするか或いは何らかの判定記号が格納されている。 Further, the schedule ID 1142 stores an ID corresponding to the schedule ID 1131 of the execution candidate table 1130 described above. Further, the part number 1143 and the recycled product determination 1144 store the component number of the component to be newly installed in the replacement operation of the component and the recycled product determination flag of the corresponding component. When the maintenance work does not include a parts replacement work, these part number 1143 and remanufactured product judgment 1144 are blank or some judgment symbols are stored.
　また、倉庫１１４５、輸送手段１１４６、納期１１４７、部品価格１１４８、輸送費１１４９には、それぞれ、交換用の部品を調達する倉庫名、輸送手段、納期、該当部品の価格、輸送に要する費用がそれぞれ格納されている。なお、上述した部品番号１１４３、再生品判定１１４４と同様に、保守作業が部品交換作業を含まない場合、これら倉庫１１４５、輸送手段１１４６、納期１１４７、部品価格１１４８、輸送費１１４９には、空白あるいは何らかの判定記号が格納されている。 In addition, the warehouse 1145, the transportation means 1146, the delivery date 1147, the part price 1148, and the transportation cost 1149 respectively include the name of the warehouse for procuring replacement parts, the transportation means, the delivery date, the price of the corresponding part, and the transportation cost. Stored. As in the case of the part number 1143 and the recycled product determination 1144 described above, when the maintenance work does not include the part replacement work, the warehouse 1145, the transportation means 1146, the delivery date 1147, the part price 1148, and the transportation cost 1149 are blank or Some kind of judgment symbol is stored.
　また、負荷率１１５０には、保守計画実施の際の鉱山機械１０における該当部位（部品交換の対象部位）の負荷率の上限が格納されている。また、運用ロス１１５１には、前記の負荷率１１５０が示す値に鉱山機械１０における負荷を制限することで生じる、顧客の経済損失が格納されている。また、作業ロス１１５２には、保守作業を実施する際の鉱山機械１０の稼働停止に伴う顧客の経済損失が格納されている。なお、調達／運用テーブル１１４０に関しては、部品の調達に係る調達テーブルと、負荷率や運用ロスなど運用に係る運用テーブルに分けて管理しても良い。 Also, the load factor 1150 stores the upper limit of the load factor of the corresponding part (part replacement target part) in the mining machine 10 when the maintenance plan is executed. Further, the operation loss 1151 stores the customer's economic loss caused by limiting the load on the mining machine 10 to the value indicated by the load factor 1150. The work loss 1152 stores the economic loss of the customer due to the stoppage of the operation of the mining machine 10 when the maintenance work is performed. The procurement / operation table 1140 may be managed separately as a procurement table for parts procurement and an operation table for operations such as load factor and operation loss.
　図１２に本実施形態における、予測結果データベース１６２の一例を示す。予測結果データベース１６２は、対象１２０１と、部位コード１２０２と、部位名１２０３と、部品番号１２０４と、再生品判定１２０５と、定期交換間隔１２０６と、平均寿命１２０７と、履歴ベース平均ＤＴ(ダウンタイム)１２０８と、稼働ベース平均ＤＴ１２０９と、サンプル数１２１０から構成されるレコードの集合体となっている。このレコードは性能予測部１５０の寿命算出機能１５１と、ダウンタイム算出機能１５２の出力である。 FIG. 12 shows an example of the prediction result database 162 in this embodiment. The prediction result database 162 includes an object 1201, a part code 1202, a part name 1203, a part number 1204, a recycled product determination 1205, a regular replacement interval 1206, an average life 1207, and a history-based average DT (downtime). 1208, an operation base average DT 1209, and a set of records composed of 1210 samples. This record is the output of the life calculation function 151 and the downtime calculation function 152 of the performance prediction unit 150.
　このうち対象１２０１は、上述した寿命算出機能１５１とダウンタイム算出機能１５２が性能予測の対象とした鉱山機械１０の範囲を特定するもので、該当鉱山機械１０らを示す、国別コード、顧客ＩＤ、サイトＩＤによって指定されている。図１２の例では、この対象１２０１としてサイトＩＤの値が設定されている。また、部位コード１２０２と部位名１２０３には、性能予測の対象とした部位を示すコードと、その名称が格納されている。また、部品番号１２０４と再生品判定１２０５には、性能予測の対象とした部品の製品番号と、再生品判定フラグが格納されている。 Among these, the target 1201 specifies the range of the mining machine 10 for which the lifetime calculation function 151 and the downtime calculation function 152 described above are targets of performance prediction, and the country code and customer ID indicating the corresponding mining machine 10 and the like. Specified by the site ID. In the example of FIG. 12, the value of the site ID is set as the target 1201. Further, the part code 1202 and the part name 1203 store a code indicating the part targeted for performance prediction and its name. Further, the part number 1204 and the recycled product determination 1205 store the product number of the component subjected to performance prediction and the recycled product determination flag.
　また、定期交換間隔１２０６には、性能予測の対象の部品、すなわち部品番号１２０４が示す部品の定期交換間隔が格納されている。この定期交換間隔１２０６の値は、例えば、部品メーカーが規定した設計上の交換間隔の値となる。また、平均寿命１２０７には、上述した寿命算出機能１５１が部品履歴データベース１５３に基づいて算出した平均寿命の値が格納されている。この平均寿命１２０７の値は、上述した定期交換間隔１２０６の値との比較対象となる。 Also, the periodic replacement interval 1206 stores the periodic replacement interval of the part whose performance is to be predicted, that is, the part indicated by the part number 1204. The value of the regular replacement interval 1206 is, for example, a design replacement interval value defined by a component manufacturer. The average life 1207 stores a value of the average life calculated by the above-described life calculation function 151 based on the component history database 153. The value of the average life 1207 is to be compared with the value of the regular replacement interval 1206 described above.
　また、履歴ベース平均ＤＴ１２０８には、上述したダウンタイム算出機能１５２が、部品履歴データベース１５３と故障履歴データベース１２４に基づいて算出した平均ダウンタイムの値が格納されている。また、稼働ベース平均ＤＴ１２０９には、上述したダウンタイム算出機能１５２が、部品履歴データベース１５３と部品稼働データベース１５４に基づいて算出した平均ダウンタイムの値が格納されている。また、サンプル数１２１０には、寿命算出機能１５１とダウンタイム算出機能１５２が処理対象とした部品の点数が格納されている。 The history-based average DT 1208 stores the average downtime value calculated by the above-described downtime calculation function 152 based on the component history database 153 and the failure history database 124. The operation base average DT 1209 stores the average downtime value calculated by the downtime calculation function 152 described above based on the component history database 153 and the component operation database 154. The number of samples 1210 stores the number of parts processed by the life calculation function 151 and the downtime calculation function 152.
　以下、本実施形態における保守計画立案方法の実際手順について図に基づき説明する。以下で説明する保守計画立案方法に対応する各種動作は、保守計画立案システム１００がメモリ１１３に読み出して実行するプログラムによって実現される。そして、これらのプログラムは、以下に説明される各種の動作を行うためのコードから構成されている。 Hereinafter, the actual procedure of the maintenance planning method according to the present embodiment will be described with reference to the drawings. Various operations corresponding to the maintenance planning method described below are realized by a program that the maintenance planning system 100 reads into the memory 113 and executes. These programs are composed of codes for performing various operations described below.
　図１３は、本実施形態における保守計画立案方法の処理手順例１を示すフロー図である。このフローは、保守計画立案支援システム１００が、監視システム１７０より、センサ１１のセンサデータの値が所定の閾値を超えたとの通知を受けたことをトリガとして、実行されるものとなる。 FIG. 13 is a flowchart showing a processing procedure example 1 of the maintenance plan planning method in the present embodiment. This flow is executed when the maintenance planning support system 100 receives a notification from the monitoring system 170 that the value of the sensor data of the sensor 11 exceeds a predetermined threshold.
　まず、処理Ｓ１３０１は、異常診断部１２０の現象診断機能１２１によって実行される。現象診断機能１２１は、監視システム１７０が検出した或る部品に関するセンサデータの異常と、現象履歴データベース１２３のセンサデータ２１０とを比較することで、監視システム１７０が検知した現象を特定し、対応する現象ＩＤ２０１、現象コード２０６、現象内容２０７といった該当現象に関する情報を現象履歴データベース１２３から抽出する。 First, the process S1301 is executed by the phenomenon diagnosis function 121 of the abnormality diagnosis unit 120. The phenomenon diagnosis function 121 identifies and responds to a phenomenon detected by the monitoring system 170 by comparing the sensor data abnormality detected by the monitoring system 170 with the sensor data 210 of the phenomenon history database 123. Information on the corresponding phenomenon such as the phenomenon ID 201, the phenomenon code 206, and the phenomenon content 207 is extracted from the phenomenon history database 123.
　現象診断機能１２１は、こうして自身が特定した現象に関する情報と、上述した監視システム１７０がセンサデータの異常を検出した部品に関する情報（例：顧客ＩＤ、サイトＩＤ、機械ＩＤ、部品シリアル番号等）とを、保守計画データベース１６１における異常診断結果テーブル１１００に出力する。 The phenomenon diagnosis function 121 includes information related to the phenomenon identified by itself, information related to a part for which the monitoring system 170 detects an abnormality in sensor data (eg, customer ID, site ID, machine ID, part serial number, etc.) Is output to the abnormality diagnosis result table 1100 in the maintenance plan database 161.
　次に、処理Ｓ１３０２は、異常診断部１２０の故障診断機能１２２によって実行される。故障診断機能１２２は、上述した処理Ｓ１３０１において抽出した現象ＩＤ２０１の値をキーとして、故障履歴データベース１２４を参照し、監視システム１７０が検知した現象に対応する故障を特定し、その故障ＩＤ２１１及び故障コード２２２を抽出する。故障診断機能１２２は、こうして抽出した故障に関する情報を、保守計画データベース１６１における異常診断結果テーブル１１００に出力する。 Next, the process S1302 is executed by the failure diagnosis function 122 of the abnormality diagnosis unit 120. The failure diagnosis function 122 refers to the failure history database 124 using the value of the phenomenon ID 201 extracted in the above-described process S1301 as a key, identifies a failure corresponding to the phenomenon detected by the monitoring system 170, and sets the failure ID 211 and the failure code. 222 is extracted. The failure diagnosis function 122 outputs information on the failure thus extracted to the abnormality diagnosis result table 1100 in the maintenance plan database 161.
　次に、処理Ｓ１３０３は、対策抽出部１３０の対策抽出機能１３１によって実行される。対策抽出機能１３１は、上述した処理Ｓ１３０２において、故障診断機能１２２によって抽出された故障ＩＤ２１１の値をキーとして、作業履歴データベース１３２を参照し、過去の同様の故障に対して実施された保守作業を特定し、その作業コード３１２を抽出する。対策抽出機能１３１は、抽出した結果を対策抽出結果テーブル１１２０に出力する。 Next, step S1303 is executed by the measure extracting function 131 of the measure extracting unit 130. The countermeasure extracting function 131 refers to the work history database 132 by using the value of the failure ID 211 extracted by the failure diagnosis function 122 in the process S1302 described above as a key, and performs maintenance work that has been performed for the same failure in the past. Then, the work code 312 is extracted. The measure extraction function 131 outputs the extracted result to the measure extraction result table 1120.
　次に、処理Ｓ１３０４は、対策抽出部１３０の対策抽出機能１３１によって実行される。対策抽出機能１３１は、処理Ｓ１３０３において抽出した作業コード３１２をキーとして、保守作業データベース１３３を参照し、該当保守作業に要する作業費用４０８と、標準作業時間４０９と、必要機材４１０と、保守員技能４１１の値を抽出する。対策抽出機能１３１は、ここで抽出した結果を対策抽出結果テーブル１１２０に出力する。 Next, step S1304 is executed by the measure extraction function 131 of the measure extraction unit 130. The countermeasure extracting function 131 refers to the maintenance work database 133 using the work code 312 extracted in the processing S1303 as a key, and the work cost 408, the standard work time 409, the necessary equipment 410, the maintenance staff skill required for the corresponding maintenance work. The value of 411 is extracted. The measure extraction function 131 outputs the result extracted here to the measure extraction result table 1120.
　次に、処理Ｓ１３０５は、計画作成部１４０の計画作成機能１４１によって実行される。計画作成機能１４１は、上述した処理Ｓ１３０４において抽出された必要機材４１０に対応する機材と、保守員技能４１１が示す技能を保有する保守員のスケジュールを、日程データベース１４３から読み込む。計画作成機能１４１は、ここで読み込んだスケジュールから、該当機材及び該当保守員が保守作業に対応出来る、すなわち他に作業予定が入っていない「空き日」を実施候補日として特定し、現時点から実施候補日までの期間ｔｘを算定する。計画作成機能１４１は、特定した実施候補日（実施候補テーブル１１３０では、'実施日'に該当）と期間ｔｘの情報を実施候補テーブル１１３０に出力する。 Next, step S1305 is executed by the plan creation function 141 of the plan creation unit 140. The plan creation function 141 reads, from the schedule database 143, the schedule of the maintenance staff who possesses the equipment corresponding to the necessary equipment 410 extracted in the above-described processing S1304 and the skills indicated by the maintenance staff skills 411. From the schedule read here, the plan creation function 141 identifies a “vacant day” that can be handled by the relevant equipment and maintenance personnel, that is, no other work schedules, as the implementation candidate date, and implements from the present time. The period tx until the candidate date is calculated. The plan creation function 141 outputs information on the identified implementation candidate date (corresponding to “execution date” in the implementation candidate table 1130) and the period tx to the implementation candidate table 1130.
　続いて処理Ｓ１３０６は、計画作成機能１４１によって実行される。計画作成機能１４１は、上述した処理Ｓ１３０３において対策抽出機能１３１によって特定された保守作業が、部品交換作業を含むものであるか否かを判定する。保守作業が部品交換作業を含まない場合（Ｓ１３０６：Ｎｏ）、計画作成機能１４１は、処理Ｓ１３０７を実行する。一方、保守作業が部品交換作業を含む場合（Ｓ１３０６：Ｙｅｓ）、計画作成機能１４１は処理Ｓ１３０８を実行する。 Subsequently, step S1306 is executed by the plan creation function 141. The plan creation function 141 determines whether the maintenance work specified by the measure extraction function 131 in the above-described process S1303 includes a parts replacement work. When the maintenance work does not include the part replacement work (S1306: No), the plan creation function 141 executes the process S1307. On the other hand, when the maintenance work includes parts replacement work (S1306: Yes), the plan creation function 141 executes the process S1308.
　次に、処理Ｓ１３０７は、計画作成部１４０の計画作成機能１４１によって実行される。計画作成機能１４１は、実施候補テーブル１１３０と、顧客知識データベース１４５とを参照して運用計画を作成し、調達／運用テーブル１１４０に出力する。この運用計画作成の詳細手順については、図１４を用いて後述する。 Next, the process S1307 is executed by the plan creation function 141 of the plan creation unit 140. The plan creation function 141 creates an operation plan with reference to the implementation candidate table 1130 and the customer knowledge database 145, and outputs the operation plan to the procurement / operation table 1140. The detailed procedure for creating the operation plan will be described later with reference to FIG.
　一方、処理Ｓ１３０８は、計画作成部１４０の計画作成機能１４１によって実行される。計画作成機能１４１は、実施候補テーブル１１３０と、部品調達データベース１４４と、顧客知識データベース１４５とを参照し、調達／運用計画を作成し、調達／運用計画テーブル１１４０に出力する。この調達／運用計画作成の詳細手順については、図１５を用いて後述する。 On the other hand, the process S1308 is executed by the plan creation function 141 of the plan creation unit 140. The plan creation function 141 creates a procurement / operation plan by referring to the execution candidate table 1130, the parts procurement database 144, and the customer knowledge database 145, and outputs the procurement / operation plan to the procurement / operation plan table 1140. Details of the procurement / operation plan creation will be described later with reference to FIG.
　次に、処理Ｓ１３０９は、性能予測部１５０によって実行される。性能予測部１５０の寿命算出機能１５１は、上述した処理Ｓ１３０８で作成した調達／運用計画に示されている交換用の部品について、部品履歴データベース１５３に基づいて該当部品の平均寿命を算定する。また、性能予測部１５０のダウンタイム算出機能１５２は、部品履歴データベース１５３と、故障履歴データベース１２４と、部品稼働データベース１５４とに基づいて、該当部品のダウンタイムを算出する。寿命算出機能１５１が算定した平均寿命の値と、ダウンタイム算出機能１５２が算定したダウンタイムの値は、それぞれの機能が予測結果データベース１６２に出力する。寿命算出機能１５１とダウンタイム算出機能１５２での各処理については図１６から図１８を用いて後述する。 Next, step S1309 is executed by the performance prediction unit 150. The life calculation function 151 of the performance prediction unit 150 calculates the average life of the corresponding part based on the part history database 153 for the replacement part indicated in the procurement / operation plan created in the above-described process S1308. Further, the downtime calculation function 152 of the performance prediction unit 150 calculates the downtime of the corresponding part based on the part history database 153, the failure history database 124, and the part operation database 154. The average lifetime value calculated by the lifetime calculation function 151 and the downtime value calculated by the downtime calculation function 152 are output to the prediction result database 162 by each function. Each process in the life calculation function 151 and the downtime calculation function 152 will be described later with reference to FIGS.
　次に、処理Ｓ１３１０は、計画作成部１４０の定期保守計画調整機能１４２によって実行される。定期保守計画調整機能１４２は、上述した計画作成機能１４１によって作成された運用／調達計画テーブル１１４０と、定期保守データベース１４６とを参照し、運用／調達計画が示す実施候補日と定期保守の実施予定日とが合致するか否か判定する。実施候補日と定期保守の実施予定日とが合致する場合（Ｓ１３１０：Ｙｅｓ）、定期保守計画調整機能１４２は、処理Ｓ１３１１を実行する。他方、実施候補日と定期保守の実施予定日とが合致しない場合（Ｓ１３１０：Ｎｏ）、定期保守計画調整機能１４２は処理Ｓ１３１２を実行する。 Next, step S1310 is executed by the regular maintenance plan adjustment function 142 of the plan creation unit 140. The regular maintenance plan adjustment function 142 refers to the operation / procurement plan table 1140 created by the above-described plan creation function 141 and the regular maintenance database 146, and performs the implementation candidate date and the scheduled maintenance execution schedule indicated by the operation / procurement plan. It is determined whether or not the date matches. If the execution candidate date matches the scheduled scheduled maintenance date (S1310: Yes), the scheduled maintenance plan adjustment function 142 executes step S1311. On the other hand, when the execution candidate date does not match the scheduled scheduled maintenance date (S1310: No), the scheduled maintenance plan adjustment function 142 executes step S1312.
　また、処理Ｓ１３１１は、計画作成部１４０の計画作成機能１４１によって実行される。計画作成機能１４１は、定期保守の実施予定日を唯一の実施候補日とし、上述した運用／調達計画が部品交換作業を含まない場合は処理Ｓ１３０７を実行して運用計画を作成する。また、計画作成機能１４１は、上述した運用／調達計画が部品交換作業を含む場合は処理Ｓ１３０８を実行して調達／運用計画を作成する。計画作成機能１４１は、こうして作成した運用／調達計画や運用計画を、調達／運用計画テーブル１１４０に出力する。 Further, the process S1311 is executed by the plan creation function 141 of the plan creation unit 140. The plan creation function 141 sets the scheduled maintenance execution date as the only execution candidate date, and executes the processing S1307 to create an operation plan when the above-described operation / procurement plan does not include parts replacement work. The plan creation function 141 creates a procurement / operation plan by executing step S1308 when the above-described operation / procurement plan includes parts replacement work. The plan creation function 141 outputs the operation / procurement plan and operation plan thus created to the procurement / operation plan table 1140.
　次に、処理Ｓ１３１２は、出力管理部１６０によって実行される。出力管理部１６０は、これまでの手順において出力管理部１６０の保守計画データベース１６１および予測結果データベース１６２に保存された保守計画及び予測結果を、クライアント端末１９０に出力する。クライアント端末１９０は、保守計画及び予測結果をディスプレイ上に出力し、該当ユーザによる保守計画の評価、検討に供する。 Next, the process S1312 is executed by the output management unit 160. The output management unit 160 outputs the maintenance plan and the prediction result stored in the maintenance plan database 161 and the prediction result database 162 of the output management unit 160 in the procedure so far to the client terminal 190. The client terminal 190 outputs the maintenance plan and the prediction result on the display, and is used for evaluation and examination of the maintenance plan by the corresponding user.
　図１４は本実施形態における、運用計画作成手順（処理Ｓ１３０７）の一例である。処理Ｓ１３０７は、計画作成部１４０の計画作成機能１４１によって実行される。また、計画作成機能１４１は、上述した処理Ｓ１３０１から処理Ｓ１３０５において出力された、保守計画データベース１６１の異常診断結果テーブル１１００と、対策抽出結果テーブル１１２０と、実施候補テーブル１１３０を参照可能であるものとする。 FIG. 14 is an example of an operation plan creation procedure (processing S1307) in the present embodiment. The process S1307 is executed by the plan creation function 141 of the plan creation unit 140. Further, the plan creation function 141 can refer to the abnormality diagnosis result table 1100, the countermeasure extraction result table 1120, and the implementation candidate table 1130 of the maintenance plan database 161 output in the processing S1301 to S1305 described above. To do.
　この場合、処理Ｓ１４０１において、計画作成機能１４１は、実施候補テーブル１１３０における各レコードのうち、例えば最も実施日１１３４が早い実施候補に関するものを抽出し（実施候補ｘ）、抽出したレコード中より、現時点から実施日１１３４までの時間ｔｘの値を取得し、メモリ１１３に格納する。 In this case, in the process S1401, the plan creation function 141 extracts, for example, the one related to the implementation candidate with the earliest implementation date 1134 among the records in the implementation candidate table 1130 (execution candidate x). From time to date 1134 is acquired and stored in the memory 113.
　次に処理Ｓ１４０２において、計画作成機能１４１は、上述で抽出したレコードが示す対策ＩＤ１１３２の値をキーに、対策抽出結果テーブル１１２０で異常ＩＤ１１２２を特定し、この異常ＩＤ１１２２をキーに異常診断結果テーブル１１００にて、保守対象である部品に関する、顧客ＩＤ、サイトＩＤ、現象コード、現象内容、故障コード、故障内容といった情報を特定する。また、計画作成機能１４１は、ここで特定した情報を、顧客知識データベース１４５の残寿命テーブル７１０に照合して、該当部品の残寿命７２１の値を取得する。計画作成機能１４１は、こうして取得した残寿命の値と、現時点から実施日１１３４までの時間ｔｘとを比較し、保守作業実施までに故障が発生するか否かを判定する。 Next, in process S1402, the plan creation function 141 specifies the abnormality ID 1122 in the countermeasure extraction result table 1120 using the value of the countermeasure ID 1132 indicated by the record extracted above as a key, and uses the abnormality ID 1122 as a key to diagnose the abnormality diagnosis result table 1100. Then, information such as a customer ID, a site ID, a phenomenon code, a phenomenon content, a failure code, and a failure content relating to a part to be maintained is specified. Further, the plan creation function 141 collates the information specified here with the remaining life table 710 of the customer knowledge database 145, and acquires the value of the remaining life 721 of the corresponding part. The plan creation function 141 compares the remaining life value acquired in this way with the time tx from the current time to the execution date 1134, and determines whether or not a failure occurs before the maintenance work is performed.
　上述の判定の結果が、残寿命＞ｔｘであった場合（Ｓ１４０２：残寿命＞ｔｘ）、計画作成機能１４１は、実施日１１３４までに該当部品は故障しないと推定し、該当部品の負荷率＝'１００'とし、次なる処理Ｓ１４０５を実行する。 When the result of the above determination is the remaining life> tx (S1402: remaining life> tx), the plan creation function 141 estimates that the corresponding part does not fail by the implementation date 1134, and the load factor of the corresponding part = Set to “100”, and the next process S1405 is executed.
　一方、上述の判定の結果が、残寿命＜ｔｘの場合（Ｓ１４０２：残寿命＜ｔｘ）、計画作成機能１４１は、実施候補ｘの実施日１１３４までに該当部品が故障すると推定し、次なる処理Ｓ１４０３を実行する。なお、上述の処理Ｓ１４０２で取得した残寿命が'０'である場合とは、突発故障によって既に該当部品の機能が停止していることを意味する。この場合（Ｓ１４０２：残寿命＝０）、計画作成機能１４１は、処理Ｓ１４０４において該当部品の負荷率＝'０'をセットし、以降の処理Ｓ１４０５を実行する。 On the other hand, when the result of the above determination is the remaining life <tx (S1402: remaining life <tx), the plan creation function 141 estimates that the corresponding part will fail by the execution date 1134 of the execution candidate x, and the next process S1403 is executed. The case where the remaining life acquired in the above-described process S1402 is “0” means that the function of the corresponding part has already stopped due to a sudden failure. In this case (S1402: remaining life = 0), the plan creation function 141 sets the load factor = '0' of the corresponding part in process S1404, and executes the subsequent process S1405.
　ここで、残寿命と実施日１１３４との関係を図１９Ａを用いて説明する。図１９Ａは残寿命と現時点から実施日１１３４までの時間ｔｘとの関係を示している。例えば、実施候補１は、ｔ１＜残寿命、の関係にあり、保守作業の実施日１１３４までに部品の故障が起きないと予想される。また、実施候補２は、ｔ２＞残寿命、の関係にあり、保守作業の実施日１１３４までに部品が故障することが予想される。この実施候補２に基づいて保守作業を実施するためには、該当部品に関して異常検知後に該当部品の負荷率を制限した運用を行い、残寿命を延長させる必要がある。 Here, the relationship between the remaining life and the implementation date 1134 will be described with reference to FIG. 19A. FIG. 19A shows the relationship between the remaining life and the time tx from the present time to the implementation date 1134. For example, the execution candidate 1 has a relationship of t1 <remaining life, and it is predicted that no component failure will occur by the execution date 1134 of the maintenance work. Further, the execution candidate 2 has a relationship of t2> remaining life, and it is expected that a component will fail by the execution date 1134 of the maintenance work. In order to carry out maintenance work based on this execution candidate 2, it is necessary to extend the remaining life by performing an operation in which the load factor of the relevant part is limited after the abnormality is detected with respect to the relevant part.
　処理Ｓ１４０３において、計画作成機能１４１は、顧客知識データベース１４５の残寿命テーブル７１０を参照し、保守作業の実施日までに該当部品が故障しない負荷率、つまり残寿命＞ｔｘとなる最大の負荷率を特定する。例えば、残寿命がｔｘより２日短い場合、残寿命テーブル７１０において、負荷率が'１００'の場合より、残寿命が３日長くなる、負荷率'９０'を特定すればよい。 In process S1403, the plan creation function 141 refers to the remaining life table 710 of the customer knowledge database 145, and determines the load factor at which the corresponding part does not fail by the date of the maintenance work, that is, the maximum load factor that satisfies the remaining life> tx. Identify. For example, when the remaining life is 2 days shorter than tx, the remaining life table 710 may specify the load factor “90” that is 3 days longer than the case where the load factor is “100”.
　また、処理Ｓ１４０５において、計画作成機能１４１は、運用ロスと作業ロスを算出する。上述したように、保守作業の実施日まで部品の負荷率を制限して運用する必要がある場合、その間は鉱山機械１０がフル稼働していない期間となって顧客の利益は減少し、経済損失を生じることになる。図１９Ｂに示すように、この経済損失を運用ロスとする。計画作成機能１４１は、処理Ｓ１４０２あるいは処理Ｓ１４０３、処理Ｓ１４０４で取得した負荷率を、顧客知識データベース１４５の運用ロステーブル７００に照合し、該当運用ロス７０７の値を特定する。また、計画作成機能１４１は、特定した運用ロス７０７の値を、上述したｔｘ（現時点から保守作業の実施日１１３４までの猶予時間）の値に乗算し、運用ロスを算定する。 In step S1405, the plan creation function 141 calculates an operation loss and a work loss. As described above, when it is necessary to operate with the load factor of parts limited until the date of the maintenance work, the mining machine 10 is not fully operated during that period, and the customer's profit is reduced, resulting in economic loss. Will result. As shown in FIG. 19B, let this economic loss be an operational loss. The plan creation function 141 collates the load factor acquired in step S1402 or step S1403 and step S1404 with the operation loss table 700 of the customer knowledge database 145, and specifies the value of the corresponding operation loss 707. In addition, the plan creation function 141 calculates the operation loss by multiplying the value of the specified operation loss 707 by the above-described value of tx (the grace time from the current time to the maintenance work implementation date 1134).
　また、保守作業中は鉱山機械１０を停止するので、保守作業中における該当部品の負荷率は'０'となる。その場合、その間は鉱山機械１０が停止した期間となって顧客の利益は減少し、経済損失を生じる。この経済損失を作業ロスとする。計画作成機能１４１は、対策抽出結果テーブル１１２０から抽出した標準作業時間１１２５と、顧客知識データベース１４５の運用ロステーブル７００における負荷率'０'の場合の運用ロス７０７の値とを乗算し作業ロスを算出する。 Moreover, since the mining machine 10 is stopped during the maintenance work, the load factor of the corresponding part during the maintenance work becomes “0”. In that case, during that period, the mining machine 10 is stopped, and the profit of the customer is reduced, resulting in an economic loss. This economic loss is defined as work loss. The plan creation function 141 multiplies the standard work time 1125 extracted from the measure extraction result table 1120 by the value of the operation loss 707 in the case of the load factor “0” in the operation loss table 700 of the customer knowledge database 145 to calculate the work loss. calculate.
　処理Ｓ１４０６において、計画作成機能１４１は、実施候補ｘに対応する、負荷率と、運用ロスと、作業ロスを、保守計画データベース１６１の調達／運用計画テーブル１１４０に出力する。なお、処理Ｓ１３０７では、部品交換作業を含まない保守作業を対象としており、この場合、計画作成機能１４１は、調達／運用計画テーブル１１４０における、部品番号１１４３と、再生品判定１１４４と、倉庫１１４５と、輸送手段１１４６と、納期１１４７と、部品価格１１４８と、輸送費１１４９の値は空白とするか、あるいは何らかの判定記号を出力する。 In process S1406, the plan creation function 141 outputs the load factor, operation loss, and work loss corresponding to the execution candidate x to the procurement / operation plan table 1140 of the maintenance plan database 161. In step S1307, maintenance work that does not include parts replacement work is targeted. In this case, the plan creation function 141 uses the part number 1143, the recycled product determination 1144, the warehouse 1145, and the like in the procurement / operation plan table 1140. The values of the transportation means 1146, the delivery date 1147, the part price 1148, and the transportation cost 1149 are blank, or some judgment symbol is output.
　次に処理Ｓ１４０７では、計画作成機能１４１は、保守計画データベース１６１の実施候補テーブル１１３０を参照し、まだ上述した処理Ｓ１４０１～Ｓ１４０６の処理を未実施である他の実施候補があるか否か判定する。実施候補が他に存在する場合（Ｓ１４０７：Ｙｅｓ）、計画作成機能１４１は、再び処理Ｓ１４０１を実行する。他方、実施候補が他に存在しない場合（Ｓ１４０７：Ｎｏ）、計画作成機能１４１は、当該フローすなわち処理Ｓ１３０７を終了する。 Next, in process S1407, the plan creation function 141 refers to the execution candidate table 1130 of the maintenance plan database 161, and determines whether there are other execution candidates that have not yet been processed in the above-described processes S1401 to S1406. . If there are other execution candidates (S1407: Yes), the plan creation function 141 executes the process S1401 again. On the other hand, when there is no other implementation candidate (S1407: No), the plan creation function 141 ends the flow, that is, the process S1307.
　図１５は本実施形態における、調達／運用計画の作成手順（処理Ｓ１３０８）の一例である。処理Ｓ１３０８は、計画作成部１４０の計画作成機能１４１によって実行される。また、計画作成機能１４１は、上述した処理Ｓ１３０１から処理Ｓ１３０５において出力された、保守計画データベース１６１の異常診断結果テーブル１１００と、対策抽出結果テーブル１１２０と、実施候補テーブル１１３０を参照可能である。 FIG. 15 is an example of a procurement / operation plan creation procedure (processing S1308) in the present embodiment. The process S1308 is executed by the plan creation function 141 of the plan creation unit 140. Further, the plan creation function 141 can refer to the abnormality diagnosis result table 1100, the countermeasure extraction result table 1120, and the execution candidate table 1130 of the maintenance plan database 161 output in the processing S1301 to S1305 described above.
　まず、処理Ｓ１５０１において、計画作成機能１４１は、実施候補テーブル１１３０より、例えば、実施日１１３４が最も直近となっている実施候補を抽出し（実施候補ｘ）、現時点から実施日１１３４までの時間ｔｘを該当レコード中より抽出し、メモリ１１３に格納する。 First, in process S1501, the plan creation function 141 extracts, for example, the implementation candidate whose implementation date 1134 is the latest from the implementation candidate table 1130 (execution candidate x), and the time tx from the current time to the implementation date 1134. Are extracted from the corresponding record and stored in the memory 113.
　次に処理Ｓ１５０２において、計画作成機能１４１は、保守計画データベース１６１の現象診断結果テーブル１１００から、異常が検出された部品のＩＤである、部品シリアル番号１１０７の値を抽出する。計画作成機能１４１は、ここで抽出した部品シリアル番号１１０７の値をキーにして、部品履歴データベース１５３を参照し、部品番号と、再生品判定の各値を抽出する。また、計画作成機能１４１は、抽出した部品番号と再生品判定の各値をキーにして、部品調達データベース１４４の部品在庫テーブル６００を読み込み、対応する部品の在庫６０６の値を取得しメモリ１１３に格納する。 Next, in process S1502, the plan creation function 141 extracts the value of the part serial number 1107, which is the ID of the part in which the abnormality is detected, from the phenomenon diagnosis result table 1100 of the maintenance plan database 161. The plan creation function 141 uses the value of the part serial number 1107 extracted here as a key to refer to the part history database 153 and extracts the part number and each value of the recycled product determination. Further, the plan creation function 141 reads the parts inventory table 600 of the parts procurement database 144 using the extracted part number and each value of the remanufactured product as a key, acquires the value of the inventory 606 of the corresponding part, and stores it in the memory 113. Store.
　処理Ｓ１５０３において、計画作成機能１４１は、上述したＳ１５０２で読み込んだ部品調達データベース１４４の在庫６０６の値から、在庫が一つ以上ある倉庫６０５を選択する。 In process S1503, the plan creation function 141 selects a warehouse 605 having one or more stocks from the value of the stock 606 in the parts procurement database 144 read in S1502.
　次に処理Ｓ１５０４において、計画作成機能１４１は、上述した実施候補ｘの異常ＩＤをキーに、異常診断結果テーブル１１００にて該当レコードを特定し、このレコード中より、異常が検出された鉱山機械１０が稼働しているサイトのサイトＩＤ１１０５の値を抽出する。また、計画作成機能１４１は、このサイトＩＤ１１０５の値を、顧客知識データベース１４５における顧客テーブル７３０に照合して、'ｓｉｔｅＡ'といったサイト名７３４の値を特定する。計画作成機能１４１は、このサイト名（＝"ｓｉｔｅＡ"）と、上述の処理Ｓ１５０３で選択した倉庫６０５の値をキーにし、部品調達データベース１４４の輸送手段テーブル６１０における輸送先６１２の値が上述のサイト名で、輸送手段テーブル６１０における倉庫６１１の値が上述の倉庫６０５の値である、輸送手段６１３を選択する。 Next, in process S1504, the plan creation function 141 specifies the corresponding record in the abnormality diagnosis result table 1100 using the abnormality ID of the execution candidate x described above as a key, and the mining machine 10 in which the abnormality is detected from this record. The value of the site ID 1105 of the site where is operating is extracted. Further, the plan creation function 141 collates the value of the site ID 1105 with the customer table 730 in the customer knowledge database 145 and specifies the value of the site name 734 such as “siteA”. The plan creation function 141 uses the site name (= “siteA”) and the value of the warehouse 605 selected in the above-described processing S1503 as a key, and the value of the transportation destination 612 in the transportation means table 610 of the parts procurement database 144 is the above-described value. With the site name, the transportation means 613 in which the value of the warehouse 611 in the transportation means table 610 is the value of the warehouse 605 described above is selected.
　処理Ｓ１５０５において、計画作成機能１４１は、上述した処理Ｓ１５０２で抽出した部品の価格と、上述した処理Ｓ１５０４で選択した輸送手段と、この輸送手段に対応する輸送費、及び納期の各値を、保守計画データベース１６１の調達／運用計画テーブル１１４０の該当欄に格納する。 In process S1505, the plan creation function 141 maintains the values of the parts extracted in process S1502, the transport means selected in process S1504, the transport costs corresponding to the transport means, and the delivery date values. Stored in the corresponding column of the procurement / operation plan table 1140 of the plan database 161.
　また、処理Ｓ１５０６において、計画作成機能１４１は、保守作業の実施日１１３４までに、保守作業に用いる交換用の部品が倉庫から納品されるか否かを判定する。この場合、計画作成機能１４１は、調達／運用計画テーブル１１４０に格納した納期１１４７の値と、上述した現時点から実施日１１３４までの時間ｔｘとを比較し、納期＞ｔｘであった場合（Ｓ１５０６：Ｙｅｓ）、保守作業の実施日１１３４までに該当部品は納品される予定であると推定し、処理Ｓ１５０７を実行する。他方、納期＜ｔｘであった場合（Ｓ１５０６：Ｙｅｓ）、計画作成機能１４１は、保守作業の実施日１１３４までに該当部品の納品がされないと推定し、処理Ｓ１５１５を実行する。 In step S1506, the plan creation function 141 determines whether or not replacement parts used for the maintenance work are delivered from the warehouse by the maintenance work execution date 1134. In this case, the plan creation function 141 compares the value of the delivery date 1147 stored in the procurement / operation plan table 1140 with the above-described time tx from the current time to the implementation date 1134, and if delivery date> tx (S1506: Yes), it is estimated that the corresponding part is scheduled to be delivered by the maintenance work implementation date 1134, and the process S1507 is executed. On the other hand, if the delivery date <tx (S1506: Yes), the plan creation function 141 estimates that the relevant part will not be delivered by the maintenance work implementation date 1134, and executes the process S1515.
　続いて処理Ｓ１５０７において、計画作成機能１４１は、上述した処理Ｓ１４０２と同様に、残寿命とｔｘとを比較し、保守作業の実施日１１３４までに交換対象の部品に故障が発生するか否かを判定する。残寿命＞ｔｘの場合（Ｓ１５０７：残寿命＞ｔｘ）、計画作成機能１４１は、該当部品を通常通りにフル稼働させても保守作業の実施日まで問題ないと判定し、調達／運用計画テーブル１１４０における、該当部品の負荷率＝"１００"と設定し、処理Ｓ１５１０を実行する。 Subsequently, in process S1507, the plan creation function 141 compares the remaining life with tx in the same manner as in process S1402 described above, and determines whether or not a failure occurs in the part to be replaced by the maintenance work implementation date 1134. judge. When the remaining life> tx (S1507: remaining life> tx), the plan creation function 141 determines that there is no problem until the date of maintenance work even if the corresponding part is fully operated as usual, and the procurement / operation plan table 1140 In step S1510, the load factor of the corresponding part is set to “100”.
　他方、残寿命＜ｔｘの場合（Ｓ１５０７：残寿命＜ｔｘ）、計画作成機能１４１は、処理Ｓ１５０８を実行する。また、残寿命＝"０"の場合（Ｓ１５０７：残寿命＝０）、計画作成機能１４１は、Ｓ１５０９において、調達／運用計画テーブル１１４０における該当部品の負荷率＝"０"と設定し、処理Ｓ１５１０を実行する。 On the other hand, if the remaining life <tx (S1507: remaining life <tx), the plan creation function 141 executes processing S1508. If the remaining life = “0” (S1507: remaining life = 0), the plan creation function 141 sets the load factor = “0” of the corresponding part in the procurement / operation plan table 1140 in S1509, and processing S1510. Execute.
　処理Ｓ１５０８において、計画作成機能１４１は、上述した処理Ｓ１４０３と同様に、顧客知識データベース１４５の残寿命テーブル７１０を参照し、保守作業の実施日１１３４までに該当部品が故障しない負荷率、つまり残寿命＞ｔｘとなる最大の負荷率を特定する。例えば、残寿命がｔｘより２日短い場合、残寿命テーブル７１０において、負荷率が'１００'の場合より、残寿命が３日長くなる、負荷率'９０'を特定すればよい。 In process S1508, the plan creation function 141 refers to the remaining life table 710 of the customer knowledge database 145 in the same manner as in the above-described process S1403. The maximum load factor that satisfies> tx is specified. For example, when the remaining life is 2 days shorter than tx, the remaining life table 710 may specify the load factor “90” that is 3 days longer than the case where the load factor is “100”.
　また、処理Ｓ１５１０において、計画作成機能１４１は、上述した処理Ｓ１４０５と同様に運用ロスと作業ロスを算出する。上述したように、保守作業の実施日まで部品の負荷率を制限して運用する必要がある場合、その間は鉱山機械１０がフル稼働していない期間となって顧客の利益は減少し、経済損失を生じることになる。計画作成機能１４１は、処理Ｓ１５０７あるいは処理Ｓ１５０８、処理Ｓ１５０９で取得した負荷率を、顧客知識データベース１４５の運用ロステーブル７００に照合し、該当運用ロス７０７の値を特定する。また、計画作成機能１４１は、特定した運用ロス７０７の値を、上述したｔｘ（現時点から保守作業の実施日１１３４までの猶予時間）の値に乗算し、運用ロスを算定する。　また、保守作業中は鉱山機械１０を停止するので、保守作業中における該当部品の負荷率は'０'となる。その場合、その間は鉱山機械１０が停止した期間となって顧客の利益は減少し、経済損失を生じる。この経済損失を作業ロスとする。計画作成機能１４１は、対策抽出結果テーブル１１２０から抽出した標準作業時間１１２５と、顧客知識データベース１４５の運用ロステーブル７００における負荷率'０'の場合の運用ロス７０７の値とを乗算し作業ロスを算出する。 In step S1510, the plan creation function 141 calculates operation loss and work loss in the same manner as in step S1405 described above. As described above, when it is necessary to operate with the load factor of parts limited until the date of the maintenance work, the mining machine 10 is not fully operated during that period, and the customer's profit is reduced, resulting in economic loss. Will result. The plan creation function 141 collates the load factor acquired in step S1507, step S1508, or step S1509 with the operation loss table 700 of the customer knowledge database 145, and specifies the value of the corresponding operation loss 707. In addition, the plan creation function 141 calculates the operation loss by multiplying the value of the specified operation loss 707 by the above-described value of tx (the grace time from the current time to the maintenance work implementation date 1134). Moreover, since the mining machine 10 is stopped during the maintenance work, the load factor of the corresponding part during the maintenance work becomes “0”. In that case, during that period, the mining machine 10 is stopped, and the profit of the customer is reduced, resulting in an economic loss. This economic loss is defined as work loss. The plan creation function 141 multiplies the standard work time 1125 extracted from the measure extraction result table 1120 by the value of the operation loss 707 in the case of the load factor “0” in the operation loss table 700 of the customer knowledge database 145 to calculate the work loss. calculate.
　また、処理Ｓ１５１１において、計画作成機能１４１は、上述した処理Ｓ１４０６と同様に調達／運用計画を保守計画データベース１６１の調達／運用テーブル１１４０に出力する。なお、計画作成機能１４１は、調達／運用計画テーブル１１４０における、部品番号１１４３と、再生品判定１１４４には、上述した処理Ｓ１５０２で抽出した対応部品の部品シリアル番号１１０７の値と、再生品判定フラグをそれぞれ格納する。また、計画作成機能１４１は、調達／運用計画テーブル１１４０における倉庫１１４５には、上述の処理Ｓ１５０３で選択した倉庫６０５の値を格納する。また、計画作成機能１４１は、調達／運用計画テーブル１１４０における輸送手段１１４６には、上述の処理Ｓ１５０４で選択した輸送手段を格納し、同様に、納期１１４７と、部品価格１１４８と、輸送費１１４９には、処理Ｓ１５０５で格納した納期と、価格と、輸送費をそれぞれ格納する。 In process S1511, the plan creation function 141 outputs the procurement / operation plan to the procurement / operation table 1140 of the maintenance plan database 161 in the same manner as the above-described process S1406. The plan creation function 141 includes a part number 1143 in the procurement / operation plan table 1140 and a remanufactured product determination 1144. The value of the part serial number 1107 of the corresponding part extracted in the above-described processing S1502 and a remanufactured product determination flag. Is stored respectively. Further, the plan creation function 141 stores the value of the warehouse 605 selected in the above-described processing S1503 in the warehouse 1145 in the procurement / operation plan table 1140. Further, the plan creation function 141 stores the transportation means selected in the above-described processing S1504 in the transportation means 1146 in the procurement / operation plan table 1140, and similarly, the delivery date 1147, the part price 1148, and the transportation cost 1149 are stored. Stores the delivery date, price, and transportation cost stored in step S1505.
　次に、処理Ｓ１５１２において、計画作成機能１４１は、部品調達データベース１４４の輸送手段テーブル６１０を参照し、処理Ｓ１５０４にて示したサイト名（＝"ｓｉｔｅＡ"）と倉庫６０５の各値が、輸送手段テーブル６１０における輸送先６１２と倉庫６１１の各値と一致する、他の輸送手段６１３（上述の処理Ｓ１５０４で選択されていないもの）があるか否かを判定する。他の輸送手段６１３がある場合（Ｓ１５１２：Ｙｅｓ）、計画作成機能１４１は、再び処理Ｓ１５０４を実行する。他方、他の輸送手段６１３がない場合（Ｓ１５１２：Ｎｏ）、計画作成機能１４１は、処理Ｓ１５１３を実行する。 Next, in process S1512, the plan creation function 141 refers to the transport means table 610 of the parts procurement database 144, and the site name (= “siteA”) and the values in the warehouse 605 shown in process S1504 are the transport means. It is determined whether there is another transport means 613 (not selected in the above-described processing S1504) that matches the values of the transport destination 612 and the warehouse 611 in the table 610. When there is another transportation means 613 (S1512: Yes), the plan creation function 141 executes the process S1504 again. On the other hand, when there is no other transportation means 613 (S1512: No), the plan creation function 141 executes the process S1513.
　処理Ｓ１５１３において、計画作成機能１４１は、上述した処理Ｓ１５０３と同様の手法で部品調達データベース１４４の部品在庫テーブル６００を参照し、対象部品の在庫が他の倉庫にあるか否かを判定する。対象部品が他の倉庫にある場合（Ｓ１５１３：Ｙｅｓ）、計画作成機能１４１は、処理Ｓ１５０３を実行する。他方、対象部品が他の倉庫にない場合（Ｓ１５１３：Ｎｏ）、計画作成機能１４１は、処理Ｓ１５１４を実行する。 In process S1513, the plan creation function 141 refers to the part inventory table 600 of the part procurement database 144 by the same method as in the above-described process S1503, and determines whether the inventory of the target part is in another warehouse. When the target part is in another warehouse (S1513: Yes), the plan creation function 141 executes the process S1503. On the other hand, when the target part is not in another warehouse (S1513: No), the plan creation function 141 executes the process S1514.
　処理Ｓ１５１４において、計画作成機能１４１は、部品調達データベース１４４の互換部品テーブル６２０を参照し、上述の処理Ｓ１５０２で在庫確認した部品の互換部品があるか否かを判定する。互換部品の有無を判定する場合、計画作成機能１４１は、処理Ｓ１５０２で在庫確認した部品に関する、サイトＩＤ１１０５、機械ＩＤ１１０６、故障コード１１１０などの値（異常診断結果テーブル１１００にて抽出）をキーに、例えば、故障履歴データベース１２４にてレコードを検索し、該当レコードより、鉱山機械１０の型名２１５、部位コード２１７、部位名２１８を特定する。計画作成機能１４１は、ここで特定した型名２１５、部位コード２１７、部位名２１８の各値をキーに、互換部品テーブル６２０で検索を実行し、型名６２１、部位コード６２２、部位名６２３の各値は共通するが、上述の処理Ｓ１５０２で在庫確認した部品の部品番号とは異なる部品、すなわち互換部品があるか判定する。この処理Ｓ１５１４において、互換部品があると判定した場合（Ｓ１５１４：Ｙｅｓ）、計画作成機能１４１は、再び処理Ｓ１５０２を実行する。他方、互換部品がないと判定した場合（Ｓ１５１４：Ｎｏ）、計画作成機能１４１は、処理Ｓ１５１５を実行する。 In process S1514, the plan creation function 141 refers to the compatible part table 620 of the part procurement database 144 and determines whether there is a compatible part of the part whose inventory has been confirmed in the above-described process S1502. When determining the presence or absence of compatible parts, the plan creation function 141 uses values such as the site ID 1105, machine ID 1106, and fault code 1110 (extracted from the abnormality diagnosis result table 1100) related to the parts whose inventory has been confirmed in step S1502 as keys. For example, the record is searched in the failure history database 124, and the model name 215, the part code 217, and the part name 218 of the mining machine 10 are specified from the corresponding record. The plan creation function 141 executes a search in the compatible part table 620 using the values of the type name 215, the part code 217, and the part name 218 specified here as keys, and the type name 621, the part code 622, and the part name 623 are searched. Although each value is common, it is determined whether there is a part that is different from the part number of the part whose inventory has been confirmed in the above-described processing S1502, that is, a compatible part. In the process S1514, when it is determined that there is a compatible part (S1514: Yes), the plan creation function 141 executes the process S1502 again. On the other hand, when it is determined that there is no compatible part (S1514: No), the plan creation function 141 executes the process S1515.
　また、処理Ｓ１５１５において、計画作成機能１４１は、上述した処理Ｓ１４０６と同様に保守計画データベース１６１の実施候補テーブル１１３０を参照し、未処理の他の実施候補があるか否かを判定する。この判定にて未処理の実施候補が他に存在する場合（Ｓ１５１５：Ｙｅｓ）、計画作成機能１４１は、再び処理Ｓ１５０１を実行する。他方、未処理の実施候補が他に存在しない場合（Ｓ１５１５：Ｎｏ）、計画作成機能１４１は、このフローすなわち処理Ｓ１３０８を終了する。 In step S1515, the plan creation function 141 refers to the execution candidate table 1130 of the maintenance plan database 161 as in the above-described step S1406, and determines whether there are other unprocessed execution candidates. If there is another unprocessed execution candidate in this determination (S1515: Yes), the plan creation function 141 executes the process S1501 again. On the other hand, when there is no other unprocessed execution candidate (S1515: No), the plan creation function 141 ends this flow, that is, the process S1308.
　続いて、図１６から図１８に、本実施形態における寿命・ダウンタイムシミュレーション実施手順（処理Ｓ１３０９）の一例を示す。寿命・ダウンタイムシミュレーションの処理は、図１６に示す寿命シミュレーションＳ１３０９（ａ）と、図１７に示すダウンタイムシミュレーションＳ１３０９（ｂ）と、図１８に示すダウンタイムシミュレーションＳ１３０９（ｃ）から構成されるが、これらは互いに独立で、どの処理から実行しても構わない。なお、図１６に示す寿命予測シミュレーションは性能予測部１５０の寿命算出機能１５１が実行し、図１７、１８に示すダウンタイムシミュレーションはダウンタイム算出機能１５２が実行する。また、寿命算出機能１５１およびダウンタイム算出機能１５２は、上述した処理Ｓ１３０１から処理Ｓ１３０８によって出力された保守計画データベース１６１を参照可能である。 Subsequently, FIG. 16 to FIG. 18 show an example of a life / downtime simulation execution procedure (processing S1309) in the present embodiment. The life / downtime simulation process includes a life simulation S1309 (a) shown in FIG. 16, a downtime simulation S1309 (b) shown in FIG. 17, and a downtime simulation S1309 (c) shown in FIG. These are independent of each other and may be executed from any processing. The life prediction simulation shown in FIG. 16 is executed by the life calculation function 151 of the performance prediction unit 150, and the downtime simulation shown in FIGS. 17 and 18 is executed by the downtime calculation function 152. Further, the life calculation function 151 and the downtime calculation function 152 can refer to the maintenance plan database 161 output from the above-described processing S1301 to processing S1308.
　図１６は、本実施形態における寿命シミュレーションの実施手順の一例である。この寿命シミュレーションのフローにおいて、寿命算出機能１５１は、部品履歴データベース１５３を参照し、過去に使用、交換された部品の寿命を算出する。この場合、処理Ｓ１６０１において、寿命算出機能１５１は部品履歴データベース１５３を読み込む。 FIG. 16 is an example of a procedure for performing a life simulation in the present embodiment. In this life simulation flow, the life calculation function 151 refers to the part history database 153 and calculates the life of parts used and replaced in the past. In this case, in the process S1601, the life calculation function 151 reads the component history database 153.
　また、処理Ｓ１６０２において、寿命算出機能１５１は、保守計画データベース１６１の調達／運用計画テーブル１１４０から、保守作業に用いる交換用の部品の部品番号１１４３と、再生品判定１１４４の各値を抽出し、これらの値をキーとして、部品履歴データベース１５３を検索し、部品番号１１４３と再生品判定１１４４の各値と、部品番号９０４３と再生品判定９０５の各値が一致する該当部品に関するレコードを特定する。また、寿命算出機能１５１は、ここで抽出した各レコードより状況フラグ９１０の値を読み取り、この値が「交換済」となっている部品のレコードを特定し、そのデータを例えばメモリ１１３にて格納する。 In process S1602, the life calculation function 151 extracts the part number 1143 of the replacement part used for the maintenance work and each value of the recycled product determination 1144 from the procurement / operation plan table 1140 of the maintenance plan database 161, Using these values as keys, the parts history database 153 is searched, and records relating to the corresponding parts in which the values of the part number 1143 and the recycled product determination 1144 and the values of the part number 9043 and the recycled product determination 905 match are specified. Further, the life calculation function 151 reads the value of the status flag 910 from each record extracted here, specifies the record of the component whose value is “replaced”, and stores the data in the memory 113, for example. To do.
　続いて処理Ｓ１６０３において、寿命算出機能１５１は、上述の処理Ｓ１６０２で得た各部品のデータにおける取付日時９１１と取外日時９１２の値の差を、該当部品の寿命として算出する。また、寿命算出機能１５１は、処理Ｓ１６０２で抽出した全データについて、寿命算出の処理すなわち処理Ｓ１６０３を実行したか判定する、処理Ｓ１６０４の判定を実行することで、処理Ｓ１６０２で抽出したデータ全てに対して上述の寿命算出を実行する。 Subsequently, in process S1603, the life calculation function 151 calculates the difference between the values of the attachment date / time 911 and the removal date / time 912 in the data of each part obtained in the above-described process S1602, as the service life of the corresponding part. In addition, the lifetime calculation function 151 executes the determination in step S1604 to determine whether the lifetime calculation process, that is, the process S1603 has been executed for all the data extracted in the process S1602, and thereby to all the data extracted in the process S1602. The above-described life calculation is executed.
　また処理Ｓ１６０５において、寿命算出機能１５１は、処理Ｓ１６０３で各データに関して算出した寿命の値の平均値を算定し、これを、予測結果データベース１６２における平均寿命１２０８の値として出力する。また、寿命算出機能１５１は、処理Ｓ１６０３で特定したデータの個数をサンプル数１２１１として予測結果データベース１６２に出力する。 In process S1605, the life calculation function 151 calculates an average value of the life values calculated for each data in process S1603, and outputs this as the value of the average life 1208 in the prediction result database 162. Further, the life calculation function 151 outputs the number of data specified in the process S1603 to the prediction result database 162 as the number of samples 1211.
　また、処理Ｓ１６０６において、寿命算出機能１５１は、上述の処理Ｓ１６０２にて抽出した、保守作業に用いる交換用の部品の部品番号１１４３と再生品判定１１４４の各値のセットで特定される部品とは異なる他の部品を用いた計画が、保守計画データベース１６１の調達／運用テーブル１１４０に格納されているか否か判定する。他の部品を用いた計画が存在した場合（Ｓ１６０６：Ｙｅｓ）、寿命算出機能１５１は、その部品を処理対象として、再び処理Ｓ１６０２以降の処理を実行する。他方、他の部品を用いた計画が存在しなかった場合（Ｓ１６０６：Ｎｏ）、寿命算出機能１５１は、このフローすなわち処理Ｓ１３０９（ａ）を終了する。 In process S1606, the life calculation function 151 uses the part number 1143 of the replacement part used for maintenance work extracted in the above-described process S1602 and the part specified by the set of each value of the recycled product determination 1144. It is determined whether or not a plan using other different parts is stored in the procurement / operation table 1140 of the maintenance plan database 161. When there is a plan using another part (S1606: Yes), the life calculation function 151 executes the process from step S1602 again with the part as a processing target. On the other hand, when there is no plan using other parts (S1606: No), the life calculation function 151 ends this flow, that is, the process S1309 (a).
　図１７は、本実施形態における、故障履歴データベース１２４と部品履歴データベース１５３をベースにダウンタイムシミュレーションを実施する手順の一例である。この場合のダウンタイムシミュレーションにおいて、ダウンタイム算出機能１５２は、故障履歴データベース１２４と、部品履歴データベース１５３を参照し、過去に使用、交換された部品のダウンタイムを算出する。 FIG. 17 is an example of a procedure for performing a downtime simulation based on the failure history database 124 and the component history database 153 in the present embodiment. In the downtime simulation in this case, the downtime calculation function 152 refers to the failure history database 124 and the component history database 153 to calculate the downtime of the parts that have been used and replaced in the past.
　まず、処理Ｓ１７０１及び処理Ｓ１７０２において、ダウンタイム算出機能１５２は、上述した処理Ｓ１６０１及び処理Ｓ１６０２と同様に、部品履歴データベース１５３を読み込み、保守計画データベース１６１の調達／運用計画テーブル１１４０から、保守作業に用いる交換用の部品の部品番号１１４３と、再生品判定１１４４の各値を抽出し、これらの値をキーとして、部品履歴データベース１５３を検索し、部品番号１１４３と再生品判定１１４４の各値と、部品番号９０４３と再生品判定９０５の各値が一致する該当部品に関するレコードを特定する。また、ダウンタイム算出機能１５２は、ここで抽出した各レコードより状況フラグ９１０の値を読み取り、この値が「交換済」となっている部品のレコードを特定し、そのデータを例えばメモリ１１３にて格納する。 First, in the processes S1701 and S1702, the downtime calculation function 152 reads the parts history database 153 and performs maintenance work from the procurement / operation plan table 1140 of the maintenance plan database 161 in the same manner as the above-described processes S1601 and S1602. The part number 1143 of the replacement part to be used and each value of the recycled product judgment 1144 are extracted, and the parts history database 153 is searched using these values as keys, and the part number 1143 and each value of the recycled product judgment 1144, A record relating to the corresponding part in which the values of the part number 9043 and the recycled product determination 905 match is specified. Further, the downtime calculation function 152 reads the value of the status flag 910 from each record extracted here, identifies the record of the part whose value is “replaced”, and stores the data in the memory 113, for example. Store.
　また、処理Ｓ１７０３において、ダウンタイム算出機能１５２は、上述の処理Ｓ１７０２で得たデータから部品シリアル番号を取得する。また、処理Ｓ１７０４において、ダウンタイム算出機能１５２は、取得した部品シリアル番号をキーとして故障履歴データベース１２４を参照する。 In step S1703, the downtime calculation function 152 acquires a part serial number from the data obtained in step S1702. In step S1704, the downtime calculation function 152 refers to the failure history database 124 using the acquired component serial number as a key.
　また、故障履歴データベース１２４を参照したダウンタイム算出機能１５２は、処理Ｓ１７０５において、上述の部品シリアル番号が示す部品に故障履歴があるか否かを判定する。該当部品に故障履歴がある場合（ｓ１７０５：Ｙｅｓ）、ダウンタイム算出機能１５２は、処理Ｓ１７０６を実行する。一方、該当部品に故障履歴がない場合（ｓ１７０５：Ｎｏ）、ダウンタイム算出機能１５２は、処理Ｓ１７０８を実行する。 Also, the downtime calculation function 152 referring to the failure history database 124 determines whether or not the component indicated by the above-described component serial number has a failure history in the process S1705. When there is a failure history in the corresponding part (s1705: Yes), the downtime calculation function 152 executes the process S1706. On the other hand, when there is no failure history in the corresponding part (s1705: No), the downtime calculation function 152 executes step S1708.
　次に処理Ｓ１７０６において、ダウンタイム算出機能１５２は、故障履歴データベース１２４の故障ＩＤ２１１をキーとして作業履歴データベース１３２を参照し、該当作業の対応開始日時３０３と対応終了日時３０４の差を、故障ＩＤ２１１に対応するダウンタイムとして算定する。また、処理Ｓ１７０７において、ダウンタイム算出機能１５２は、上述の処理Ｓ１７０５で故障があると判定されたすべての部品についてダウンタイムを算出したか否かを判定する。ダウンタイムを判定していない故障がある場合（Ｓ１７０７：Ｎｏ）、ダウンタイム算出機能１５２は、処理Ｓ１７０６を繰り返し実行し、全ての故障についてダウンタイムを算出した場合（Ｓ１７０７：Ｙｅｓ）、処理Ｓ１７０７を実行する。 In step S1706, the downtime calculation function 152 refers to the work history database 132 using the failure ID 211 in the failure history database 124 as a key, and sets the difference between the corresponding start date 303 and the corresponding end date 304 of the corresponding task as the failure ID 211. Calculate as the corresponding downtime. In step S1707, the downtime calculation function 152 determines whether or not the downtime has been calculated for all the parts determined to have a failure in the above-described step S1705. When there is a failure whose downtime is not determined (S1707: No), the downtime calculation function 152 repeatedly executes the process S1706, and when the downtime is calculated for all failures (S1707: Yes), the process S1707 is performed. Execute.
　また、処理Ｓ１７０８において、ダウンタイム算出機能１５２は、上述の処理Ｓ１７０２で部品履歴データベース１５３から抽出したデータの最終行に達したかを判定する。最終行に達している場合（Ｓ１７０８：Ｙｅｓ）、ダウンタイム算出機能１５２は、処理Ｓ１７０９を実行する。他方、まだデータがある場合（Ｓ１７０８：Ｎｏ）、ダウンタイム算出機能１５２は、再び処理Ｓ１７０３以降の処理を実行して、該当部品のダウンタイムを算出する。 In step S1708, the downtime calculation function 152 determines whether the last line of the data extracted from the part history database 153 in step S1702 has been reached. If the last line has been reached (S1708: Yes), the downtime calculation function 152 executes step S1709. On the other hand, when there is still data (S1708: No), the downtime calculation function 152 executes the processing after the processing S1703 again to calculate the downtime of the corresponding part.
　また、処理Ｓ１７０９において、ダウンタイム算出機能１５２は、上述の処理Ｓ１７０６で算出したダウンタイムの平均値を算出し、これを性能予測データベース１６２の履歴ベース平均ＤＴ１２０９の値として格納する。 Also, in the process S1709, the downtime calculation function 152 calculates the average value of the downtime calculated in the above-described process S1706, and stores this as the value of the history base average DT1209 of the performance prediction database 162.
　また、処理Ｓ１７１０において、ダウンタイム算出機能１５２は、処理Ｓ１６０６と同様にして保守計画データベース１６１の調達／運用テーブル１１４０に、他の部品を用いた計画があるか否かを判定する。他の部品を用いた計画がある場合（Ｓ１７１０：Ｙｅｓ）、ダウンタイム算出機能１５２は、その部品を処理対象の部品として、上述の処理Ｓ１７０２以降の処理を再び実行する。他方、他の部品を用いた計画がない場合（Ｓ１７１０：Ｎｏ）、ダウンタイム算出機能１５２は、このフローすなわち処理Ｓ１３０９（ｂ）を終了する。 In step S1710, the downtime calculation function 152 determines whether there is a plan using other parts in the procurement / operation table 1140 of the maintenance plan database 161 in the same manner as in step S1606. When there is a plan using other parts (S1710: Yes), the downtime calculation function 152 executes the process after the above-described process S1702 again with the part as a processing target part. On the other hand, when there is no plan using other parts (S1710: No), the downtime calculation function 152 ends this flow, that is, the process S1309 (b).
　図１８は、本実施形態におけるダウンタイムシミュレーション実施手順の一例である。この場合のダウンタイムシミュレーションにおいて、ダウンタイム算出機能１５２は、故障履歴データベース１２４と、部品履歴データベース１５３と、部品稼働データベース１５４を参照し、過去に使用、交換された部品のダウンタイムを算出する。ここで示す実施手順（Ｓ１３０９（ｃ））の基本的な手順は、上述した処理Ｓ１３０９（ｂ）と同様である。従って、ここでは、処理Ｓ１３０９（ｂ）とは異なる手順となる、ダウンタイムの算出を行う処理Ｓ１８０４から処理Ｓ１８０７の手順と、出力を行う処理Ｓ１８０９について説明する。 FIG. 18 is an example of a downtime simulation execution procedure in the present embodiment. In the downtime simulation in this case, the downtime calculation function 152 refers to the failure history database 124, the component history database 153, and the component operation database 154, and calculates the downtime of the parts that have been used and replaced in the past. The basic procedure of the implementation procedure (S1309 (c)) shown here is the same as the above-described processing S1309 (b). Therefore, here, the procedure from the processing S1804 to the processing S1807 for calculating the downtime and the processing S1809 for the output, which are different procedures from the processing S1309 (b), will be described.
　この場合、処理Ｓ１８０４において、ダウンタイム算出機能１５２は、処理Ｓ１７０１から処理Ｓ１７０３と同様にして取得した部品シリアル番号を用いて、部品稼働データベース１５４のデータを絞り込む。 In this case, in step S1804, the downtime calculation function 152 narrows down the data in the component operation database 154 using the component serial numbers acquired in the same manner as in steps S1701 to S1703.
　また、処理Ｓ１８０５において、ダウンタイム算出機能１５２は、上述のように取り込んだ部品稼働データベース１５４の稼働実績テーブル１０１０における、期間１０１３の値を参照し、判定の単位時間を算出する（図１０Ｂの例では単位時間は１時間）。また、ダウンタイム算出機能１５２は、部品稼働データベース１５４の稼働判定テーブル１０００における、判定値１００５、判定条件１００６を参照し、稼働実績テーブル１０１０の平均値１０１４が判定値１００５の値より小さくなっているレコードを、該当部品が「停止中」のものと特定する。また、ダウンタイム算出機能１５２は、「停止中」として特定したレコード数だけ、前述の単位時間の値を積算して、「停止中」の判定となっている時間を積算する。 In step S1805, the downtime calculation function 152 refers to the value of the period 1013 in the operation result table 1010 of the component operation database 154 captured as described above, and calculates a determination unit time (example in FIG. 10B). (The unit time is 1 hour). The downtime calculation function 152 refers to the determination value 1005 and the determination condition 1006 in the operation determination table 1000 of the component operation database 154, and the average value 1014 of the operation result table 1010 is smaller than the determination value 1005. The record is identified as having the corresponding part “stopped”. Further, the downtime calculation function 152 adds the above unit time values for the number of records specified as “stopped”, and adds up the time determined to be “stopped”.
　また、処理Ｓ１８０６において、ダウンタイム算出機能１５２は、部品履歴データベース１５３の取外日時９１２と、上述した処理Ｓ１８０５での積算処理の対象となった該当レコード（稼働実績テーブル１０１０のレコード）の期間１０１３の値とを比較し、この期間１０１３の示す日付が、取外日時９１２の示す日付に達した場合（Ｓ１８０６：Ｙｅｓ）、上述した処理Ｓ１８０５の積算処理を終了し、処理Ｓ１８０７を実行する。他方、期間１０１３の示す日付が、取外日時９１２の示す日付に達していない場合（Ｓ１８０６：Ｎｏ）、ダウンタイム算出機能１５２は、処理を上述の処理Ｓ１８０５に戻す。 In the process S1806, the downtime calculation function 152 displays the removal date and time 912 of the part history database 153 and the period 1013 of the corresponding record (record of the operation result table 1010) that is the target of the integration process in the process S1805 described above. When the date indicated by this period 1013 reaches the date indicated by the removal date and time 912 (S1806: Yes), the integration process of the above-described process S1805 is terminated, and the process S1807 is executed. On the other hand, when the date indicated by the period 1013 has not reached the date indicated by the removal date 912 (S1806: No), the downtime calculation function 152 returns the process to the above-described process S1805.
　また、処理Ｓ１８０７において、ダウンタイム算出機能１５２は、ここまでで積算した時間すなわちダウンタイムを、該当部品の稼働ベースダウンタイムとしてメモリ１１３に格納する。また、ダウンタイム算出機能１５２は、処理Ｓ１８０７までの処理を、処理Ｓ１８０２で得たデータ全てについて実行するまで繰り返す（ｓ１８０８）。 In step S1807, the downtime calculation function 152 stores the time accumulated so far, that is, the downtime, in the memory 113 as the operation base downtime of the corresponding part. Further, the downtime calculation function 152 repeats the process up to the process S1807 until it is executed for all the data obtained in the process S1802 (s1808).
　処理Ｓ１８０９において、ダウンタイム算出機能１５２は、処理Ｓ１８０２で抽出した各データについて得られたダウンタイムの平均を算出し、これを、稼働ベース平均ＤＴ１２０９として予測結果データベース１６２に格納する。 In process S1809, the downtime calculation function 152 calculates the average of the downtime obtained for each data extracted in process S1802, and stores this in the prediction result database 162 as the operation base average DT1209.
　図２０から図２２に本実施形態における出力画面例を示す。これらの出力画面は、出力管理部１６０が、例えばクライアント端末１９０からの要求に応じて、保守計画データベース１６１と予測結果データベース１６２に格納されているデータから、画面種類ごとに必要なデータを抽出し、このデータを該当画面のフォーマット（出力管理部１６０が保持）に設定して生成し、クライアント端末１９０に出力したものとなる。もちろん、必要に応じて、出力管理部１６０が画面フォーマットのデータをクライアント端末１９０に出力し、ユーザからの画面構成のカスタマイズ操作を受け付けるとしても良い。 20 to 22 show examples of output screens in this embodiment. For these output screens, the output management unit 160 extracts necessary data for each screen type from the data stored in the maintenance plan database 161 and the prediction result database 162 in response to a request from the client terminal 190, for example. This data is generated by setting the corresponding screen format (held by the output management unit 160) and outputting it to the client terminal 190. Of course, if necessary, the output management unit 160 may output screen format data to the client terminal 190 to accept a screen configuration customization operation from the user.
　図２０は保守計画の候補一覧を表示する画面２０００である。この画面２０００において、総費用や平均寿命、履歴ベース平均ＤＴ、稼働ベース平均ＤＴの昇順あるいは降順で各レコードを列挙している。また、こうした列挙基準に基づいて、ユーザがレコードの並び替えをできるよう、総費用や平均寿命、履歴ベース平均ＤＴ、稼働ベース平均ＤＴのいずれかを並べ替え基準として指定するためのラジオボタン２００１が画面２０００には含まれている。ユーザはこのラジオボタン２００１を押下して、保守計画の候補間の評価を行うなどできる。また、各カラムの値を数式を用いて計算するユーザ定義の項目があってもよい。 FIG. 20 shows a screen 2000 displaying a maintenance plan candidate list. In this screen 2000, each record is listed in ascending or descending order of total cost, average life, history base average DT, and operation base average DT. In addition, a radio button 2001 for designating any one of the total cost, the average life, the history base average DT, and the operation base average DT as the sort criterion so that the user can sort the records based on the enumeration criteria. The screen 2000 is included. The user can press the radio button 2001 to perform evaluation between maintenance plan candidates. There may also be a user-defined item that calculates the value of each column using a mathematical expression.
　図２１は、図２０で示した画面２０００に表示された保守計画の候補に関する、調達／運用計画の詳細及び運用の表示画面２１００の例を示す。この画面２１００は、出力管理部１６０が、上述した画面２０００において、ある候補に関してクライアント端末１９０から詳細表示の要求を受けた際に、保守計画データベース１６１と予測結果データベース１６２に格納されているデータから、該当画面に必要なデータを抽出し、このデータを該当画面のフォーマットに設定して生成し、クライアント端末１９０に出力したものとなる。この画面２１００は、対象となる調達／運用計画に関する、計画ＩＤ、実施日、機械ＩＤ、部品番号、倉庫、輸送手段、納期、部品価格、輸送費の他に、保守作業に用いる交換用の部品が再生品か否かを示す再生品判定、保守作業実施までの該当部品の負荷率、保守作業の実施日までの時間ｔ、保守作業時間、残寿命、運用ロス、作業ロスといった値を視覚的に表示することで、保守作業に対するユーザの理解を支援する。 FIG. 21 shows an example of the procurement / operation plan details and operation display screen 2100 regarding the maintenance plan candidates displayed on the screen 2000 shown in FIG. This screen 2100 is obtained from the data stored in the maintenance plan database 161 and the prediction result database 162 when the output management unit 160 receives a request for detailed display from the client terminal 190 for a certain candidate in the screen 2000 described above. The data necessary for the corresponding screen is extracted, the data is set in the format of the corresponding screen, generated, and output to the client terminal 190. This screen 2100 shows a replacement part used for maintenance work in addition to the plan ID, execution date, machine ID, part number, warehouse, transportation means, delivery date, part price, and transportation cost regarding the target procurement / operation plan. Visually show values such as whether the product is a remanufactured product, the judgment of the remanufactured product, the load factor of the relevant part until the maintenance work is performed, the time t until the date of the maintenance work, the maintenance work time, the remaining life, the operation loss, and the work loss By displaying on the screen, the user's understanding of the maintenance work is supported.
　図２２は、保守作業に係る費用の配分を表す画面２２００を示している。この画面２２００は、例えば、保守契約において、保守を実施する事業者と、鉱山機械１０を運用する顧客との間で、保守費用の分担が定まっている場合などに利用するとよい。例えば、顧客が負担する費用は交換用の部品代のみで、その他の保守費用は保守事業者が負担するような契約の場合、部品価格と、部品価格を除くその他の総費用とで比較検討できるとよい。従って、画面２２００には、総費用、部品価格、輸送費、運用ロス、作業費用、作業ロス、ユーザ定義の昇順あるいは降順で各レコードの並び替えをできるよう、総費用、部品価格、輸送費、運用ロス、作業費用、作業ロス、ユーザ定義のいずれかを並べ替え基準として指定するためのラジオボタン２２０１が画面２２００には含まれている。ユーザはこのラジオボタン２２０１を押下して、保守計画の候補間の評価を費用面で比較して行うことなどができる。また、各カラムの値を数式を用いて計算するユーザ定義の項目があってもよい。 FIG. 22 shows a screen 2200 showing the allocation of expenses related to the maintenance work. This screen 2200 may be used, for example, when a maintenance cost share is determined between a maintenance company and a customer who operates the mining machine 10 in a maintenance contract. For example, in the case of a contract in which the customer bears only the replacement parts cost and other maintenance costs are borne by the maintenance company, the part price can be compared with the other total costs excluding the part price. Good. Therefore, the total cost, the part price, the transportation cost, the operation loss, the operation cost, the work loss, the total cost, the part price, the transportation cost, The screen 2200 includes a radio button 2201 for designating any one of the operation loss, work cost, work loss, and user definition as a sorting criterion. The user can press the radio button 2201 to compare the maintenance plan candidates with each other in terms of cost. There may also be a user-defined item that calculates the value of each column using a mathematical expression.
　以下、図２３から図２９を用いて、顧客にとって最適と推定される保守計画候補を出力する実施例について簡単に説明する。図２３は、本実施例において、顧客にとって最適と推定される保守計画候補を提案する出力管理部２３００の構成例を示す図である。出力管理部２３００は、保守計画評価機能２３０１と、顧客ポリシー推定機能２３０２と、保守計画データベース２３０３と、予測結果データベース２３０４と、計画評価データベース２３０５（第１０データベース）から構成される。保守計画評価機能２３０１は、複数の保守計画を、複数の評価指標及び顧客ポリシーから順位付けする。顧客ポリシー推定機能２３０２は、顧客の保守計画選択履歴から、顧客がどのような保守計画を選択する傾向にあるかを推定する。ここでは、顧客の傾向を顧客ポリシーと呼ぶ。保守計画データベース２３０３及び予測結果データベース２３０４は、それぞれ保守計画データベース１６１及び予測結果データベース１６２と同じ構造である。計画評価データベース２３０５は、保守計画評価機能２３０１による保守計画評価の結果を格納する保守計画評価テーブル２４００と、顧客ポリシー推定機能２３０２が参照する保守選択履歴テーブル２４１０から構成される。 Hereinafter, an example of outputting a maintenance plan candidate estimated to be optimal for a customer will be briefly described with reference to FIGS. FIG. 23 is a diagram illustrating a configuration example of the output management unit 2300 that proposes a maintenance plan candidate that is estimated to be optimal for the customer in the present embodiment. The output management unit 2300 includes a maintenance plan evaluation function 2301, a customer policy estimation function 2302, a maintenance plan database 2303, a prediction result database 2304, and a plan evaluation database 2305 (tenth database). The maintenance plan evaluation function 2301 ranks a plurality of maintenance plans from a plurality of evaluation indexes and customer policies. The customer policy estimation function 2302 estimates what maintenance plan the customer tends to select from the maintenance plan selection history of the customer. Here, customer trends are called customer policies. The maintenance plan database 2303 and the prediction result database 2304 have the same structure as the maintenance plan database 161 and the prediction result database 162, respectively. The plan evaluation database 2305 includes a maintenance plan evaluation table 2400 that stores the result of the maintenance plan evaluation by the maintenance plan evaluation function 2301 and a maintenance selection history table 2410 that is referred to by the customer policy estimation function 2302.
　以下では例として、部品価格１１４８、輸送費１１４９、運用ロス１１５１、作業ロス１１５２の総和を保守費用とし、保守費用、稼働ベース平均ＤＴ１２０９を評価指標として保守計画を評価する処理を説明する。 Hereinafter, as an example, a description will be given of a process for evaluating a maintenance plan using the sum of a part price 1148, a transportation cost 1149, an operation loss 1151, and a work loss 1152 as a maintenance cost and using the maintenance cost and the operation base average DT1209 as an evaluation index.
　図２４の保守計画評価テーブル２４００は、計画ＩＤ２４０１と、評価指標である保守費用２４０２と、稼働ベース平均ＤＴ２４０３と、保守計画評価機能２３０１によって算出されるスコア２４１２、スコアＡ２４０５、スコアＢ２４０６を格納する。スコアＡ２４０５は保守計画候補をパレート最適解集合を用いたトレードオフ分析によって評価したスコア、スコアＢ２４０６は保守計画候補を顧客ポリシーとの一致度で評価したスコアであり、スコア２４０４はスコアＡ２４０５及びスコアＢ２４０６から算出する保守計画の総合スコアである。スコアＡ、スコアＢ、総合スコアの算出式は後述する。 The maintenance plan evaluation table 2400 in FIG. 24 stores a plan ID 2401, a maintenance cost 2402 as an evaluation index, an operation base average DT 2403, and a score 2412, a score A 2405, and a score B 2406 calculated by the maintenance plan evaluation function 2301. The score A 2405 is a score obtained by evaluating the maintenance plan candidate by trade-off analysis using the Pareto optimal solution set, the score B 2406 is a score obtained by evaluating the maintenance plan candidate based on the degree of coincidence with the customer policy, and the score 2404 is a score A 2405 and a score B 2406. This is the overall score of the maintenance plan calculated from The calculation formulas for score A, score B, and total score will be described later.
　また、図２５の保守選択履歴テーブル２４１０は、過去に起きた異常ＩＤ２４１１と、異常に対して行った対策ＩＤ２４１２と、異常発生時に顧客に表示した保守計画候補の計画ＩＤ２４１３と、保守計画候補の評価指標である保守費用２４０２、稼働ベース平均ＤＴ２４１５と、顧客が選択した実施履歴２４１６を格納する。保守選択履歴テーブル２４１０によって、過去の異常－対策－保守計画候補－顧客が選択した保守計画が紐付けられる。 The maintenance selection history table 2410 of FIG. 25 includes an abnormality ID 2411 that has occurred in the past, a countermeasure ID 2412 that has been taken for the abnormality, a maintenance plan candidate plan ID 2413 that is displayed to the customer when the abnormality occurs, and a maintenance plan candidate evaluation. The maintenance cost 2402, which is an index, the operation base average DT 2415, and the implementation history 2416 selected by the customer are stored. The maintenance selection history table 2410 links the past abnormality-measures-maintenance plan candidate-maintenance plan selected by the customer.
　図２６を用いて、保守計画候補の中から顧客にとって最適な保守計画を推定し、提案する処理Ｓ２５００を説明する。Ｓ２５０１で保守計画評価機能２３０１は、保守計画データベース２３０３の調達／運用テーブル１１４０及び予測結果データベース２３０４から、保守費用と、平均寿命１２０７と、稼働ベース平均ＤＴ１２０９を評価指標として抽出し、保守費用と、稼働ベース平均ＤＴ１２０９は小さいほど良いので、逆数を取る。次に各評価指標の最大値が１となるように規格化を行う。これらの操作により、各指標の最良の値は１となる。 Referring to FIG. 26, a process S2500 for estimating and proposing the optimum maintenance plan for the customer from the maintenance plan candidates will be described. In S2501, the maintenance plan evaluation function 2301 extracts the maintenance cost, the average life 1207, and the operation base average DT1209 as evaluation indexes from the procurement / operation table 1140 and the prediction result database 2304 of the maintenance plan database 2303, and the maintenance cost, The smaller the operating base average DT 1209 is, the better. Next, normalization is performed so that the maximum value of each evaluation index is 1. By these operations, the best value of each index becomes 1.
　Ｓ２５０２で保守計画評価機能２３０１は、規格化した３つの評価指標を用いて、複数の保守計画候補からパレート最適解集合を求める。保守費用、稼働ベース平均ＤＴ１２０９の二つの評価軸を用いて、パレート最適解集合を求めた一例を図２７の２６０１に示す。 In S2502, the maintenance plan evaluation function 2301 obtains a Pareto optimal solution set from a plurality of maintenance plan candidates using three standardized evaluation indexes. An example of obtaining a Pareto optimal solution set using two evaluation axes of maintenance cost and operation base average DT1209 is shown in 2601 of FIG.
　Ｓ２５０３で顧客ポリシー推定機能２３０２は、保守選択履歴テーブル２４１０から、過去に起きた異常・故障・作業の組に対してどのような保守計画候補が挙げられ、その中から顧客がどの保守計画を選択したかを読み込み、規格化した各評価指標の値を抽出する。　　
　Ｓ２５０４で顧客ポリシー推定機能２３０２は、過去に顧客が選択した保守計画の各評価指標の値から、顧客ポリシーを推定する。一例として、保守費用、稼働ベース平均ＤＴの二つの評価軸における顧客ポリシーを、最小二乗法を用いて推定した結果を図２８、２６０２に示す。 In step S2503, the customer policy estimation function 2302 selects from the maintenance selection history table 2410 what maintenance plan candidates are listed for the abnormality / failure / work combinations that occurred in the past, and the customer selects which maintenance plan from among them. And read out the standardized value of each evaluation index.
In S2504, the customer policy estimation function 2302 estimates the customer policy from the values of the evaluation indexes of the maintenance plan selected by the customer in the past. As an example, FIGS. 28 and 2602 show the results of estimating customer policies on two evaluation axes of maintenance cost and operation base average DT using the least square method.
　処理Ｓ２５０５で保守計画評価機能２３０１は、各保守計画候補に対して、パレート最適解集合からスコアＡを、推定顧客ポリシーからスコアＢをそれぞれ求め、スコアＡ及びスコアＢを用いて各保守計画候補にスコアを付与し、結果を保守計画評価テーブル２４００に格納する。 In step S2505, the maintenance plan evaluation function 2301 obtains a score A from the Pareto optimal solution set and a score B from the estimated customer policy for each maintenance plan candidate, and assigns each score to each maintenance plan candidate using the score A and the score B. A score is assigned and the result is stored in the maintenance plan evaluation table 2400.
Figure JPOXMLDOC01-appb-I000001
Figure JPOXMLDOC01-appb-I000001
　パレート最適解集合から評価したスコアＡでは、計画ＩＤ＝“ＭＰ－００１”と“ＭＰ－００８”は同スコアであり、順位付けができないが、顧客ポリシーによるスコアＢを評価に加えることで、当該顧客にとっては計画ＩＤ＝“ＭＰ－００１”が優先されると推定される。さらに、スコアＡのみの評価では、“ＭＰ－００１”、“ＭＰ－００８”に劣っていた“ＭＰ－００２”が当該顧客にとって最も優先される保守計画であると推定される。 In the score A evaluated from the Pareto optimal solution set, the plan ID = “MP-001” and “MP-008” are the same score and cannot be ranked, but by adding the score B according to the customer policy to the evaluation, It is estimated that plan ID = “MP-001” is given priority to the customer. Furthermore, in the evaluation of only the score A, it is estimated that “MP-002”, which is inferior to “MP-001” and “MP-008”, is the maintenance plan most prioritized for the customer.
　処理Ｓ２５０６で出力管理部２３００は、ユーザに対して保守計画データベース２３０３、予測結果データベース２３０４、計画評価データベース２３０５に格納された保守計画、予測結果、保守計画評価結果をスコア２４０４の順に出力し、ユーザの選択を待つ。 In step S2506, the output management unit 2300 outputs the maintenance plan, prediction result, and maintenance plan evaluation result stored in the maintenance plan database 2303, the prediction result database 2304, and the plan evaluation database 2305 to the user in the order of the score 2404. Wait for the selection.
　処理Ｓ２５０７で顧客ポリシー推定機能２３０２は、ユーザの選択を保守選択履歴テーブル２４１０に格納する。 In process S2507, the customer policy estimation function 2302 stores the user's selection in the maintenance selection history table 2410.
　本実施例においては、評価指標として用いた保守費用、稼働ベース平均ＤＴの値を規格化して推定顧客ポリシーを算出した（図２８、２６０２）。これは、保守作業に用いる部品や実施する作業によって保守費用や稼働ベース平均ＤＴが大きく異なることを想定したためであるが、類似する部品、作業では保守費用、稼働ベース平均ＤＴはオーダーとして同程度となる。このような場合は、規格化を用いることなく、各評価指標の値をそのまま用いて、顧客ポリシーを推定することも考えられる。また、本実施例では単一顧客の履歴情報を元にポリシーを推定する手順を説明したが、例えば国毎、地域毎にポリシーに類似性が認められる場合は国毎、地域毎の履歴情報を纏めて評価してもよいし、同一顧客であっても、エンジンやポンプなどの部位毎に傾向が異なる場合は部位毎に細分化して評価を行ってもよい。 In this example, the estimated customer policy was calculated by standardizing the maintenance cost and operation base average DT values used as evaluation indices (FIG. 28, 2602). This is because it is assumed that the maintenance cost and the operation base average DT vary greatly depending on the parts used for the maintenance work and the work to be performed. However, the maintenance cost and the operation base average DT are about the same as orders for similar parts and operations. Become. In such a case, it is conceivable to estimate the customer policy by using the value of each evaluation index as it is without using normalization. Further, in this embodiment, the procedure for estimating a policy based on the history information of a single customer has been described. For example, if similarity is recognized for each country or region, the history information for each country or region is displayed. Evaluation may be performed collectively, or even if the customer is the same, if the tendency is different for each part such as an engine or a pump, the evaluation may be performed by subdividing each part.
　以上、本発明を実施するための最良の形態などについて具体的に説明したが、本発明はこれに限定されるものではなく、その要旨を逸脱しない範囲で種々変更可能である。 The best mode for carrying out the present invention has been specifically described above. However, the present invention is not limited to this, and various modifications can be made without departing from the scope of the present invention.
　こうした本実施形態によれば、交換部品を一義的に取り扱うことなく、同様の機能を持つ部品でも旧版部品や再生部品など複数種類の部品を考慮し、これら各種の部品を作業機械の保守計画立案に反映することができる。また、作業機械に生じた現象から異常および故障を推定し、それに応じた保守作業の特定を行うことが出来るため、様々な状況に合わせて生じうる故障を推定し、的確な保守対応を特定することが可能である。また、鉱山機械などの大型機械に於いて保守作業に要する費用や時間の点で重要な検討要素となる部品調達について、旧版部品や再生品についても考慮しつつ、納期や輸送コスト、負荷率、負荷率に応じた経済損失をトータルで踏まえた、複数パターンの保守計画の立案支援が可能となる。こうした本実施形態の保守計画立案支援技術によれば、作業機械に異常や故障を検知した際に旧版部品や再生部品及びそれらの調達計画なども考慮して、いつ、何をすべきかをその評価と共にユーザに提供し、保守計画立案業務を支援することが出来る。 According to the present embodiment, replacement parts are not handled unambiguously, and even parts having similar functions are considered for multiple types of parts such as old edition parts and recycled parts, and these various parts are prepared as maintenance plans for work machines. Can be reflected. In addition, abnormalities and failures can be estimated from phenomena occurring in work machines, and maintenance operations can be identified accordingly. Therefore, it is possible to estimate failures that may occur in accordance with various situations and identify appropriate maintenance measures. It is possible. In addition, regarding parts procurement, which is an important consideration in terms of cost and time required for maintenance work in large machines such as mining equipment, taking into account old version parts and remanufactured parts, delivery date, transportation cost, load factor, It is possible to support the planning of multiple patterns of maintenance plans based on the total economic loss according to the load factor. According to the maintenance planning support technology of this embodiment, when an abnormality or failure is detected in the work machine, the old version parts, the refurbished parts, and the procurement plan thereof are also taken into consideration, and what is to be evaluated. Along with this, it can be provided to users to support maintenance planning work.
　したがって本実施形態によれば、保守員のスキル等に依存せずに効果的な保守計画を効率的に立案可能となる。 Therefore, according to this embodiment, it is possible to efficiently formulate an effective maintenance plan without depending on the skills of maintenance personnel.
　本明細書の記載により、少なくとも次のことが明らかにされる。すなわち、本実施形態の保守計画立案支援システムにおいて、前記記憶装置は、各作業機械の、故障時における負荷率別の残寿命および負荷率低下に伴う作業機械使用者における経済損失の情報とを対応づけて保持する第５データベースをさらに備えるものであり、前記演算装置は、前記作業機械に関する前記現象の情報が含む、作業機械ないし現象発生箇所の部品の負荷率の情報を前記第５データベースに照合して、前記作業機械ないし現象発生箇所の部品の残寿命を推定し、当該残寿命が、現時点から前記保守実施候補日までの猶予期間を下回る程度に応じて低下させた負荷率を該当作業機械における負荷率とし、当該負荷率での経済損失を前記第５データベースにて特定し、前記低下させた負荷率および当該負荷率での経済損失の情報を、前記保守計画情報に含めて出力装置に出力する処理を更に実行するものである、としてもよい。これによれば、故障が生じている部品が保守実施日まで稼働し続けられる程度に低下させるべき負荷率を特定し、この負荷率で部品を稼働させた際の顧客側の経済損失をユーザに提示することが可能となり、ユーザ側では、保守計画を決定する際に重要な、事業継続性や経済性といった面で顧客側への配慮を行いやすくなる。 記載 At least the following will be made clear by the description in this specification. That is, in the maintenance planning support system of the present embodiment, the storage device corresponds to the remaining life of each work machine according to the load factor at the time of failure and the information on the economic loss of the work machine user accompanying the load factor decrease. And further comprising a fifth database to be held, wherein the arithmetic device collates information on a load factor of a work machine or a part where the phenomenon occurs, included in the information on the phenomenon related to the work machine, with the fifth database. Then, the remaining life of the work machine or the part where the phenomenon occurs is estimated, and the load factor which is reduced according to the extent that the remaining life is less than the grace period from the current time to the maintenance execution candidate date , The economic loss at the load factor is specified in the fifth database, and the reduced load factor and the economic loss information at the load factor are specified. The maintenance plan in which information further executes a process of outputting to the output device, including a may be. According to this, the load factor that should be reduced to the extent that the failed part can continue to operate until the date of maintenance is identified, and the economic loss on the customer side when the component is operated at this load factor is reported to the user. This makes it possible for the user side to easily consider the customer side in terms of business continuity and economy, which is important when determining a maintenance plan.
　また、本実施形態の保守計画立案支援システムにおいて、前記記憶装置は、個々の部品ないしその旧版部品または再生部品の、作業機械への取付及び作業機械からの取外に関する情報を格納した第６データベースを更に備えるものであり、前記演算装置は、前記保守計画情報が示す、前記保守作業に用いる部品ないしその旧版部品または再生部品に関し、前記第６データベースにおける、同一種類の部品ないしその旧版部品または再生部品の取付と取外の履歴を特定し、当該特定した取付と取外との間の時間を寿命として算出し、当該寿命の情報を前記保守計画情報に含めて出力装置に出力する処理を更に実行するものである、としてもよい。これによれば、保守計画にて採用する交換用の部品が、どれほどの寿命を備えるものかユーザに提示することで、ユーザ側では、例えば、部品コストと寿命のバランスを考えた保守計画立案が行いやすくなるといった効果がある。 Further, in the maintenance planning support system of the present embodiment, the storage device stores a sixth database that stores information related to attachment to and removal from the work machine of individual parts or old version parts or regenerated parts thereof. The computing device relates to a part used for the maintenance work or an old version part or a reproduction part thereof indicated by the maintenance plan information, and the same type of part or the old version part or the reproduction thereof in the sixth database. A process of identifying the history of component attachment and removal, calculating the time between the identified attachment and removal as a lifetime, and including the information on the lifetime in the maintenance plan information and outputting to the output device It may be executed. According to this, by presenting to the user how long the replacement parts used in the maintenance plan will have, the user can create a maintenance plan that takes into account, for example, the balance between parts cost and life. It has the effect of making it easier to do
　また、本実施形態の保守計画立案支援システムにおいて、前記演算装置は、前記保守計画情報が示す、前記保守作業に用いる部品ないしその旧版部品または再生部品に関し、前記第１データベースにおける、同一種類の部品ないしその旧版部品または再生部品の、故障の情報の有無を特定し、故障の情報が存在する場合に、該当故障に対して実施された保守作業の情報を前記第１データベースにて特定し、該当保守作業の情報が示す、作業開始から作業終了までの間の時間をダウンタイムとして算定し、当該ダウンタイムの情報を、前記保守計画情報に含めて出力装置に出力する処理を更に実行するものである、としてもよい。これによれば、保守計画にて採用する交換用の部品が、過去の保守作業によれば、どれほどのダウンタイムの発生が見込まれるのかユーザに提示することで、ユーザ側では、例えば、部品コストとダウンタイムのバランスを考えた保守計画立案が行いやすくなるといった効果がある。 Further, in the maintenance plan planning support system of the present embodiment, the arithmetic unit is related to a part used for the maintenance work indicated by the maintenance plan information or an old version part or a recycled part thereof, and the same type of parts in the first database. Also, the presence or absence of failure information of the old version parts or remanufactured parts is specified, and when the failure information exists, the information on the maintenance work performed for the corresponding failure is specified in the first database, and the corresponding The time between the work start and work end indicated by the maintenance work information is calculated as downtime, and the process of including the downtime information in the maintenance plan information and outputting it to the output device is further executed. There may be. According to this, by presenting to the user the amount of downtime expected for replacement parts used in the maintenance plan according to past maintenance work, on the user side, for example, the part cost This also has the effect of making it easier to create a maintenance plan that takes into account the balance between the system and downtime.
　また、本実施形態の保守計画立案支援システムにおいて、前記記憶装置は、作業機械の部品に設置したセンサによる、該当部品の挙動に関する測定値を格納した第７データベースと、作業機械の部品の稼働有無を判定する前記センサによる測定値の条件を格納した第８データベースとを更に備えるものであり、前記演算装置は、前記保守計画情報が示す、前記保守作業に用いる部品ないしその旧版部品または再生部品に関し、前記第７データベースにおける、同一種類の部品ないしその旧版部品または再生部品についての測定値の情報を特定し、当該特定した測定値の情報を前記第８データベースに照合して、該当部品ないしその旧版部品または再生部品の稼働停止期間をダウンタイムとして算定し、当該ダウンタイムの情報を、前記保守計画情報に含めて出力装置に出力する処理を更に実行するものである、としてもよい。これによれば、保守計画にて採用する交換用の部品が、センサデータ由来の過去の稼働実績によれば、どれほどのダウンタイムの発生が見込まれるのかユーザに提示することで、ユーザ側では、例えば、部品コストとダウンタイムのバランスを考えた保守計画立案が行いやすくなるといった効果がある。 Further, in the maintenance planning support system of the present embodiment, the storage device includes a seventh database storing measured values related to the behavior of the corresponding part by a sensor installed on the part of the work machine, and whether or not the work machine part is in operation. And an eighth database storing conditions of measurement values by the sensor for determining the sensor, and the arithmetic unit relates to a part used for the maintenance work indicated by the maintenance plan information or an old version part or a recycled part thereof In the seventh database, information on measured values for the same type of part or its old version parts or remanufactured parts is specified, and the information on the specified measured values is collated with the eighth database, and the corresponding part or its old version The downtime of parts or remanufactured parts is calculated as downtime, and the downtime information is stored in the maintenance In which further executes a process of outputting to the output device included in the image information may be. According to this, according to the past operation results derived from sensor data, the replacement part adopted in the maintenance plan is shown to the user how much downtime is expected, so on the user side, For example, there is an effect that a maintenance plan considering the balance between parts cost and downtime can be easily made.
　また、本実施形態の保守計画立案支援システムにおいて、前記記憶装置は、作業機械に関して実施予定の定期保守の日程を格納する第９データベースを更に備えるものであり、前記演算装置は、現時点から、前記保守計画情報が示す前記保守実施候補日までの期間内に、前記定期保守の日程が含まれているか前記第９データベースにて判定し、前記期間内に前記定期保守の日程が含まれている場合、該当定期保守日で前記保守実施候補日を置換し、前記保守計画情報の生成を再度実行するものである、としてもよい。これによれば、あらかじめ組まれている定期保守の機会を無駄にせず、定期保守の機会を、イレギュラーな故障発生事象に有効活用し、効率的に保守計画を立案することが可能となる。 In the maintenance plan planning support system according to the present embodiment, the storage device further includes a ninth database that stores a schedule of scheduled maintenance scheduled to be performed on the work machine. When it is determined in the ninth database whether the schedule for the regular maintenance is included in the period until the maintenance execution candidate date indicated by the maintenance plan information, and the schedule for the regular maintenance is included in the period The maintenance execution candidate date may be replaced with the corresponding regular maintenance date, and generation of the maintenance plan information may be executed again. According to this, it is possible to efficiently devise a maintenance plan by effectively utilizing the periodic maintenance opportunity for an irregular failure occurrence event without wasting the opportunity for the periodic maintenance that is set in advance.
１０　鉱山機械（作業機械）
１１　センサ
１００　保守計画立案支援システム
１１１　Ｉ／Ｏ（出力装置）
１１２　通信装置
１１３　メモリ
１１４　ＣＰＵ（演算装置）
１１５　記憶装置
１２０　異常診断部
１２３　現象履歴データベース（第１データベース）
１２４　故障履歴データベース（第１データベース）
１３０　対策抽出部
１３２　作業履歴データベース（第１データベース）
１３３　保守作業データベース（第２データベース）
１４０　計画作成部
１４３　日程データベース（第３データベース）
１４４　部品調達データベース（第４データベース）
１４５　顧客知識データベース（第５データベース）
１４６　定期保守データベース（第９データベース）
１５０　性能予測部
１５３　部品履歴データベース（第６データベース）
１５４　部品稼働データベース
１０００　稼働判定テーブル（第８データベース）
１０１０　稼働実績テーブル（第７データベース）
１６０　出力管理部
１６１　保守計画データベース
１６２　予測結果データベース
１７０　監視システム
１８０　ネットワーク
１９０　クライアント端末 10 Mining machine (work machine)
11 Sensor 100 Maintenance planning support system 111 I / O (output device)
112 Communication device 113 Memory 114 CPU (arithmetic unit)
115 Storage Device 120 Abnormality Diagnosis Unit 123 Phenomenon History Database (First Database)
124 Failure history database (first database)
130 Countermeasure extraction unit 132 Work history database (first database)
133 Maintenance work database (second database)
140 Plan creation unit 143 Schedule database (third database)
144 Parts procurement database (4th database)
145 Customer Knowledge Database (Fifth Database)
146 Regular maintenance database (9th database)
150 Performance Prediction Unit 153 Parts History Database (Sixth Database)
154 Parts Operation Database 1000 Operation Determination Table (Eighth Database)
1010 Operation result table (seventh database)
160 Output Management Unit 161 Maintenance Plan Database 162 Prediction Result Database 170 Monitoring System 180 Network 190 Client Terminal

Claims (9)

  1. 　作業機械に発生した現象の情報と、該当現象の発生後に該当作業機械に生じた故障の情報と、前記故障に対して実施された保守作業の情報と、を対応づけて保持する第１データベースと、
    　作業機械別に規定された標準の保守作業の情報を保持する第２データベースと、
    　保守作業用の各人員および各機材の稼働可能時期の情報を保持する第３データベースと、
    　保守作業に用いられる部品ないしその旧版部品または再生部品の各在庫と価格、および、輸送先別および輸送手段別の納期および輸送コストの情報とを、倉庫別に保持する第４データベースと、を格納する記憶装置と、
    　作業機械の監視システムより、ある所在地の作業機械に発生した現象の情報をネットワークを介して受信し、当該受信した現象の情報を前記第１データベースに照合して、該当現象の発生後に起こり得る故障を推定し、該当故障に際して前記作業機械に実施された保守作業の情報を第１データベースにて特定する処理と、
    　前記特定した保守作業の情報を前記第２データベースに照合し、前記作業機械に対して実施が予想される標準の保守作業の情報を特定し、該当保守作業の情報が指定する保守作業用の人員および機材の稼働可能時期を保守実施候補日として前記第３データベースで特定する処理と、
    　前記保守作業の情報が指定する部品ないしその旧版部品または再生部品を在庫し、該当部品ないしその旧版部品または再生部品を、該当作業機械の所在地に輸送手段により納品する場合の納期が、現時点から前記保守実施候補日までの猶予期間に収まる倉庫と、前記納期に対応した輸送手段及びその輸送コストを、前記第４データベースで特定し、当該特定した、前記保守作業に用いる部品ないしその旧版部品または再生部品に関する、納期、倉庫、輸送手段、及び輸送コストの情報と前記保守実施候補日の情報とを保守計画情報として生成し出力装置に出力する処理と、を実行する演算装置と、
    　を備えることを特徴とする保守計画立案支援システム。     A first database that holds information on a phenomenon that has occurred in the work machine, information on a failure that has occurred in the work machine after the occurrence of the phenomenon, and information on maintenance work that has been performed on the failure in association with each other; ,
    A second database for holding information on standard maintenance work defined for each work machine;
    A third database that holds information on the availability of each person and equipment for maintenance work;
    A fourth database that holds each stock and price of parts used for maintenance work or its old version parts or remanufactured parts, and delivery date and transportation cost information for each destination and means of transportation for each warehouse is stored. A storage device;
    A failure that may occur after the occurrence of the corresponding phenomenon by receiving information on the phenomenon that has occurred in the work machine at a certain location from the monitoring system of the work machine via the network, collating the received information on the phenomenon with the first database. A process of identifying information on maintenance work performed on the work machine at the time of the failure in the first database;
    Personnel for maintenance work specified by the corresponding maintenance work information by collating the identified maintenance work information with the second database, identifying standard maintenance work information expected to be performed on the work machine And the process of specifying the operation availability time of the equipment as the maintenance execution candidate date in the third database,
    The parts specified by the maintenance work information or its old version parts or remanufactured parts are stocked, and the delivery date when the relevant parts or their old version parts or remanufactured parts are delivered to the location of the work machine by means of transportation is The warehouse that fits in the grace period until the maintenance candidate date, the transportation means corresponding to the delivery date and the transportation cost thereof are specified in the fourth database, and the specified parts used for the maintenance work or the old version parts or the reproduction thereof are identified. A processing device that executes a process for generating information on delivery date, warehouse, transportation means, and transportation cost and information on the maintenance execution candidate date as maintenance plan information and outputting it to an output device.
    A maintenance planning support system characterized by comprising:
  2. 　前記記憶装置は、
    　各作業機械の、故障時における負荷率別の残寿命および負荷率低下に伴う作業機械使用者における経済損失の情報とを対応づけて保持する第５データベースをさらに備えるものであり、
    　前記演算装置は、
    　前記作業機械に関する前記現象の情報が含む、作業機械ないし現象発生箇所の部品の負荷率の情報を前記第５データベースに照合して、前記作業機械ないし現象発生箇所の部品の残寿命を推定し、当該残寿命が、現時点から前記保守実施候補日までの猶予期間を下回る程度に応じて低下させた負荷率を該当作業機械における負荷率とし、当該負荷率での経済損失を前記第５データベースにて特定し、前記低下させた負荷率および当該負荷率での経済損失の情報を、前記保守計画情報に含めて出力装置に出力する処理を更に実行するものである、
    　ことを特徴とする請求項１に記載の保守計画立案支援システム。     The storage device
    A fifth database that holds the remaining life of each work machine according to the load factor at the time of failure and the information on the economic loss of the work machine user associated with the load factor drop in association with each other;
    The arithmetic unit is:
    The information of the phenomenon related to the work machine includes information on the load factor of the work machine or the part where the phenomenon occurred, and the fifth database is used to estimate the remaining life of the work machine or the part where the phenomenon occurs, The load factor that is reduced according to the extent that the remaining life falls below the grace period from the present time to the maintenance execution candidate date is set as the load factor in the corresponding work machine, and the economic loss at the load factor is stored in the fifth database. Identifying and reducing the load factor and the economic loss information at the load factor are included in the maintenance plan information and further output to the output device,
    The maintenance plan planning support system according to claim 1.
  3. 　前記記憶装置は、
    　個々の部品ないしその旧版部品または再生部品の、作業機械への取付及び作業機械からの取外に関する情報を格納した第６データベースを更に備えるものであり、
    　前記演算装置は、
    　前記保守計画情報が示す、前記保守作業に用いる部品ないしその旧版部品または再生部品に関し、前記第６データベースにおける、同一種類の部品ないしその旧版部品または再生部品の取付と取外の履歴を特定し、当該特定した取付と取外との間の時間を寿命として算出し、当該寿命の情報を前記保守計画情報に含めて出力装置に出力する処理を更に実行するものである、
    　ことを特徴とする請求項２に記載の保守計画立案支援システム。     The storage device
    And further comprising a sixth database storing information on attachment to and removal from the work machine of individual parts or their old version parts or recycled parts,
    The arithmetic unit is:
    Regarding the parts used in the maintenance work indicated by the maintenance plan information or the old version parts or regenerated parts thereof, the history of the attachment and removal of the same type of parts or the old version parts or the regenerated parts in the sixth database, Calculate the time between the specified attachment and removal as a lifetime, and further execute a process of including the lifetime information in the maintenance plan information and outputting to the output device.
    The maintenance planning support system according to claim 2, wherein:
  4. 　前記演算装置は、
    　前記保守計画情報が示す、前記保守作業に用いる部品ないしその旧版部品または再生部品に関し、前記第１データベースにおける、同一種類の部品ないしその旧版部品または再生部品の、故障の情報の有無を特定し、故障の情報が存在する場合に、該当故障に対して実施された保守作業の情報を前記第１データベースにて特定し、該当保守作業の情報が示す、作業開始から作業終了までの間の時間をダウンタイムとして算定し、当該ダウンタイムの情報を、前記保守計画情報に含めて出力装置に出力する処理を更に実行するものである、
    　ことを特徴とする請求項２に記載の保守計画立案支援システム。     The arithmetic unit is:
    Regarding the parts used in the maintenance work indicated by the maintenance plan information or the old version parts or regenerated parts thereof, the presence or absence of failure information of the same type of parts or the old version parts or regenerated parts in the first database is specified, When failure information exists, information on the maintenance work performed for the failure is identified in the first database, and the time from the work start to the work end indicated by the maintenance service information is shown. It is calculated as downtime, and further includes a process of including the downtime information in the maintenance plan information and outputting it to an output device.
    The maintenance planning support system according to claim 2, wherein:
  5. 　前記記憶装置は、
    　作業機械の部品に設置したセンサによる、該当部品の挙動に関する測定値を格納した第７データベースと、作業機械の部品の稼働有無を判定する前記センサによる測定値の条件を格納した第８データベースとを更に備えるものであり、
    　前記演算装置は、
    　前記保守計画情報が示す、前記保守作業に用いる部品ないしその旧版部品または再生部品に関し、前記第７データベースにおける、同一種類の部品ないしその旧版部品または再生部品についての測定値の情報を特定し、当該特定した測定値の情報を前記第８データベースに照合して、該当部品ないしその旧版部品または再生部品の稼働停止期間をダウンタイムとして算定し、当該ダウンタイムの情報を、前記保守計画情報に含めて出力装置に出力する処理を更に実行するものである、
    　ことを特徴とする請求項２に記載の保守計画立案支援システム。     The storage device
    A seventh database storing measured values related to the behavior of the corresponding parts by sensors installed on the parts of the work machine; and an eighth database storing conditions of measured values by the sensors for determining whether the parts of the work machine are operating. In addition,
    The arithmetic unit is:
    Regarding the parts used in the maintenance work indicated by the maintenance plan information or the old version parts or regenerated parts thereof, the measurement data on the same type of parts or the old version parts or the regenerated parts is specified in the seventh database, The identified measurement value information is collated with the eighth database to calculate the downtime as the downtime of the corresponding part or its old version part or remanufactured part, and the downtime information is included in the maintenance plan information. The process of outputting to the output device is further executed.
    The maintenance planning support system according to claim 2, wherein:
  6. 　前記記憶装置は、
    　作業機械に関して実施予定の定期保守の日程を格納する第９データベースを更に備えるものであり、
    　前記演算装置は、
    　現時点から、前記保守計画情報が示す前記保守実施候補日までの期間内に、前記定期保守の日程が含まれているか前記第９データベースにて判定し、前記期間内に前記定期保守の日程が含まれている場合、該当定期保守日で前記保守実施候補日を置換し、前記保守計画情報の生成を再度実行するものである、
    　ことを特徴とする請求項３～５のいずれかに記載の保守計画立案支援システム。     The storage device
    A ninth database for storing a schedule of scheduled maintenance scheduled for the work machine;
    The arithmetic unit is:
    It is determined in the ninth database whether the regular maintenance schedule is included in the period from the current time to the maintenance execution candidate date indicated by the maintenance plan information, and the regular maintenance schedule is included in the period. The maintenance execution candidate date is replaced with a corresponding regular maintenance date, and generation of the maintenance plan information is executed again.
    The maintenance planning support system according to any one of claims 3 to 5, wherein:
  7. 　前記記憶装置は、
    　保守計画の評価結果と、過去に顧客が選択した保守計画の履歴を格納する第１０データベースを更に備えるものであり、
    　前記演算装置は、
    　前記保守計画情報が含む保守計画に係る費用と、前記保守作業に用いる部品ないしその旧版部品または再生部品の寿命、あるいはダウンタイムを指標として用いて保守計画にスコアを付与、順位付けて出力する処理を更に実行するものである、
    　ことを特徴とする請求項３～６のいずれかに記載の保守計画立案支援システム。     The storage device
    A tenth database for storing the evaluation result of the maintenance plan and the history of the maintenance plan selected by the customer in the past;
    The arithmetic unit is:
    A process for assigning a score to a maintenance plan using the cost associated with the maintenance plan included in the maintenance plan information, the life of a part used in the maintenance work or its old version parts or remanufactured parts, or downtime as an index, and ranking and outputting the index Is to execute further,
    The maintenance planning support system according to any one of claims 3 to 6, wherein
  8. 　作業機械に発生した現象の情報と、該当現象の発生後に該当作業機械に生じた故障の情報と、前記故障に対して実施された保守作業の情報と、を対応づけて保持する第１データベースと、作業機械別に規定された標準の保守作業の情報を保持する第２データベースと、保守作業用の各人員および各機材の稼働可能時期の情報を保持する第３データベースと、保守作業に用いられる部品ないしその旧版部品または再生部品の各在庫と価格、および、輸送先別および輸送手段別の納期および輸送コストの情報とを、倉庫別に保持する第４データベースと、を格納する記憶装置を備えたコンピュータが、
    　作業機械の監視システムより、ある所在地の作業機械に発生した現象の情報をネットワークを介して受信し、当該受信した現象の情報を前記第１データベースに照合して、該当現象の発生後に起こり得る故障を推定し、該当故障に際して前記作業機械に実施された保守作業の情報を第１データベースにて特定する処理と、
    　前記特定した保守作業の情報を前記第２データベースに照合し、前記作業機械に対して実施が予想される標準の保守作業の情報を特定し、該当保守作業の情報が指定する保守作業用の人員および機材の稼働可能時期を保守実施候補日として前記第３データベースで特定する処理と、
    　前記保守作業の情報が指定する部品ないしその旧版部品または再生部品を在庫し、該当部品ないしその旧版部品または再生部品を、該当作業機械の所在地に輸送手段により納品する場合の納期が、現時点から前記保守実施候補日までの猶予期間に収まる倉庫と、前記納期に対応した輸送手段及びその輸送コストを、前記第４データベースで特定し、当該特定した、前記保守作業に用いる部品ないしその旧版部品または再生部品に関する、納期、倉庫、輸送手段、及び輸送コストの情報と前記保守実施候補日の情報とを保守計画情報として生成し出力装置に出力する処理と、
    　を実行することを特徴とする保守計画立案支援方法。     A first database that holds information on a phenomenon that has occurred in the work machine, information on a failure that has occurred in the work machine after the occurrence of the phenomenon, and information on maintenance work that has been performed on the failure in association with each other; A second database that holds information on standard maintenance work defined for each work machine, a third database that holds information on the availability of each person and equipment for maintenance work, and parts used for maintenance work A computer having a storage device for storing each stock and price of old version parts or recycled parts, and a fourth database that holds information on delivery date and transportation cost by transport destination and transport means by warehouse. But,
    A failure that may occur after the occurrence of the corresponding phenomenon by receiving information on the phenomenon that has occurred in the work machine at a certain location from the monitoring system of the work machine via the network, collating the received information on the phenomenon with the first database. A process of identifying information on maintenance work performed on the work machine at the time of the failure in the first database;
    Personnel for maintenance work specified by the corresponding maintenance work information by collating the identified maintenance work information with the second database, identifying standard maintenance work information expected to be performed on the work machine And the process of specifying the operation availability time of the equipment as the maintenance execution candidate date in the third database,
    The parts specified by the maintenance work information or its old version parts or remanufactured parts are stocked, and the delivery date when the relevant parts or their old version parts or remanufactured parts are delivered to the location of the work machine by means of transportation is The warehouse that fits in the grace period until the maintenance candidate date, the transportation means corresponding to the delivery date and the transportation cost thereof are specified in the fourth database, and the specified parts used for the maintenance work or the old version parts or the reproduction thereof are identified. A process for generating information on delivery date, warehouse, transportation means, transportation cost and information on the maintenance execution candidate date as parts of maintenance plan information and outputting it to an output device.
    A maintenance planning support method characterized by executing
  9. 　作業機械に発生した現象の情報と、該当現象の発生後に該当作業機械に生じた故障の情報と、前記故障に対して実施された保守作業の情報と、を対応づけて保持する第１データベースと、作業機械別に規定された標準の保守作業の情報を保持する第２データベースと、保守作業用の各人員および各機材の稼働可能時期の情報を保持する第３データベースと、保守作業に用いられる部品ないしその旧版部品または再生部品の各在庫と価格、および、輸送先別および輸送手段別の納期および輸送コストの情報とを、倉庫別に保持する第４データベースと、を格納する記憶装置を備えたコンピュータに、
    　作業機械の監視システムより、ある所在地の作業機械に発生した現象の情報をネットワークを介して受信し、当該受信した現象の情報を前記第１データベースに照合して、該当現象の発生後に起こり得る故障を推定し、該当故障に際して前記作業機械に実施された保守作業の情報を第１データベースにて特定する処理と、
    　前記特定した保守作業の情報を前記第２データベースに照合し、前記作業機械に対して実施が予想される標準の保守作業の情報を特定し、該当保守作業の情報が指定する保守作業用の人員および機材の稼働可能時期を保守実施候補日として前記第３データベースで特定する処理と、
    　前記保守作業の情報が指定する部品ないしその旧版部品または再生部品を在庫し、該当部品ないしその旧版部品または再生部品を、該当作業機械の所在地に輸送手段により納品する場合の納期が、現時点から前記保守実施候補日までの猶予期間に収まる倉庫と、前記納期に対応した輸送手段及びその輸送コストを、前記第４データベースで特定し、当該特定した、前記保守作業に用いる部品ないしその旧版部品または再生部品に関する、納期、倉庫、輸送手段、及び輸送コストの情報と前記保守実施候補日の情報とを保守計画情報として生成し出力装置に出力する処理と、
    　を実行させることを特徴とする保守計画立案支援プログラム。     A first database that holds information on a phenomenon that has occurred in the work machine, information on a failure that has occurred in the work machine after the occurrence of the phenomenon, and information on maintenance work that has been performed on the failure in association with each other; A second database that holds information on standard maintenance work defined for each work machine, a third database that holds information on the availability of each person and equipment for maintenance work, and parts used for maintenance work A computer having a storage device for storing each stock and price of old version parts or recycled parts, and a fourth database that holds information on delivery date and transportation cost by transport destination and transport means by warehouse. In addition,
    A failure that may occur after the occurrence of the corresponding phenomenon by receiving information on the phenomenon that has occurred in the work machine at a certain location from the monitoring system of the work machine via the network, collating the received information on the phenomenon with the first database. A process of identifying information on maintenance work performed on the work machine at the time of the failure in the first database;
    Personnel for maintenance work specified by the corresponding maintenance work information by collating the identified maintenance work information with the second database, identifying standard maintenance work information expected to be performed on the work machine And the process of specifying the operation availability time of the equipment as the maintenance execution candidate date in the third database,
    The parts specified by the maintenance work information or its old version parts or remanufactured parts are stocked, and the delivery date when the relevant parts or their old version parts or remanufactured parts are delivered to the location of the work machine by means of transportation is The warehouse that fits in the grace period until the maintenance candidate date, the transportation means corresponding to the delivery date and the transportation cost thereof are specified in the fourth database, and the specified parts used for the maintenance work or the old version parts or the reproduction thereof are identified. A process for generating information on delivery date, warehouse, transportation means, transportation cost and information on the maintenance execution candidate date as parts of maintenance plan information and outputting it to an output device.
    A maintenance planning support program characterized by causing
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016118853A (en) * 2014-12-19 2016-06-30 カシオ計算機株式会社 Work management system and program
KR101899259B1 (en) * 2017-09-21 2018-09-14 주식회사 현대미포조선 Apparatus for providing guide corresponding to an alarm in the ship and method thereof
KR20190066714A (en) * 2017-12-06 2019-06-14 주식회사 블루비즈 Method and apparatus for efficiently managing part
JP2019133550A (en) * 2018-02-02 2019-08-08 ファナック株式会社 Failure classification device, method for classifying failures, and failure classification program
US11099550B2 (en) 2018-02-08 2021-08-24 Fanuc Corporation Failure location specifying device, failure location specifying method, and failure location specifying program
JP2021526250A (en) * 2018-05-07 2021-09-30 ストロング フォース アイオーティ ポートフォリオ ２０１６，エルエルシー Methods and systems for data collection, learning and streaming of machine signals for analysis and maintenance of industrial Internet of Things
WO2023002553A1 (en) * 2021-07-20 2023-01-26 株式会社日立製作所 Maintenance task assistance device and method
WO2023218561A1 (en) * 2022-05-11 2023-11-16 日揮グローバル株式会社 Storage work assistance device, storage work assistance method, and program
JP7461899B2 (en) 2021-01-08 2024-04-04 日立建機株式会社 Maintenance Support System

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6926008B2 (en) * 2018-01-31 2021-08-25 株式会社日立製作所 Maintenance planning equipment and maintenance planning method
JP6955231B2 (en) * 2019-12-10 2021-10-27 ダイキン工業株式会社 Maintenance support system
JP2024146401A (en) * 2023-03-31 2024-10-15 横河電機株式会社 Recommendation method, recommendation program, and recommendation device

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002063418A (en) * 2000-08-22 2002-02-28 Komatsu Ltd Maintenance cost estimation system for work vehicle
JP2004127084A (en) * 2002-10-04 2004-04-22 Nec Fielding Ltd System, method and program for arranging failure restoration component
WO2005106139A1 (en) * 2004-04-28 2005-11-10 Komatsu Ltd. Maintenance support system for construction machine
JP2007219573A (en) * 2006-02-14 2007-08-30 Toshiba Corp Business risk prediction method and its device
JP2011197894A (en) * 2010-03-18 2011-10-06 Hitachi Ltd Server device and maintenance method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002063418A (en) * 2000-08-22 2002-02-28 Komatsu Ltd Maintenance cost estimation system for work vehicle
JP2004127084A (en) * 2002-10-04 2004-04-22 Nec Fielding Ltd System, method and program for arranging failure restoration component
WO2005106139A1 (en) * 2004-04-28 2005-11-10 Komatsu Ltd. Maintenance support system for construction machine
JP2007219573A (en) * 2006-02-14 2007-08-30 Toshiba Corp Business risk prediction method and its device
JP2011197894A (en) * 2010-03-18 2011-10-06 Hitachi Ltd Server device and maintenance method

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2016118853A (en) * 2014-12-19 2016-06-30 カシオ計算機株式会社 Work management system and program
KR101899259B1 (en) * 2017-09-21 2018-09-14 주식회사 현대미포조선 Apparatus for providing guide corresponding to an alarm in the ship and method thereof
KR20190066714A (en) * 2017-12-06 2019-06-14 주식회사 블루비즈 Method and apparatus for efficiently managing part
KR101996070B1 (en) * 2017-12-06 2019-07-04 주식회사 블루비즈 Method and apparatus for efficiently managing part
JP2019133550A (en) * 2018-02-02 2019-08-08 ファナック株式会社 Failure classification device, method for classifying failures, and failure classification program
US10838394B2 (en) 2018-02-02 2020-11-17 Fanuc Corporation Failure classifying device, failure classifying method, and failure classifying program for specifying locations of failures in a machine
US11099550B2 (en) 2018-02-08 2021-08-24 Fanuc Corporation Failure location specifying device, failure location specifying method, and failure location specifying program
JP2021526250A (en) * 2018-05-07 2021-09-30 ストロング フォース アイオーティ ポートフォリオ ２０１６，エルエルシー Methods and systems for data collection, learning and streaming of machine signals for analysis and maintenance of industrial Internet of Things
JP7445928B2 (en) 2018-05-07 2024-03-08 ストロング フォース アイオーティ ポートフォリオ ２０１６，エルエルシー Methods and systems for data collection, learning, and streaming of machine signals for analysis and maintenance using the industrial Internet of Things
JP7461899B2 (en) 2021-01-08 2024-04-04 日立建機株式会社 Maintenance Support System
WO2023002553A1 (en) * 2021-07-20 2023-01-26 株式会社日立製作所 Maintenance task assistance device and method
JP7549747B2 (en) 2021-07-20 2024-09-11 株式会社日立製作所 Maintenance work support device and method
WO2023218561A1 (en) * 2022-05-11 2023-11-16 日揮グローバル株式会社 Storage work assistance device, storage work assistance method, and program

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