JP2007004298A - Maintenance support method and maintenance support program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To prevent the occurrence of failures in equipment and to streamline the maintenance of the equipment when maintenance engineers visit the places where the equipment is installed to maintain the equipment according to visit plans. <P>SOLUTION: A management computer 21 specifies target equipment for normal maintenance on a visit schedule date specified on a visit plan, and identifies position information about destinations from an equipment information storage part 41 by identification information about the target equipment. Neighboring installed equipment installed within a predetermined range of the destinations is then specified with the equipment information storage part 41, and state information on the state of the neighboring installed equipment is acquired. With the total period (n+m days) of days (n days) from the current date to the visit schedule date and a range of days (m days) of predictive failure diagnosis, and the equipment state information, the possibility of failure occurrence in the neighboring installed equipment in the total period of the total days from the current date is predictively diagnosed, and the predictive diagnosis result is outputted. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a maintenance support method and a maintenance support program for supporting a maintenance work in which a maintenance staff visits an installation location of equipment to perform maintenance of the equipment.

  For image forming devices installed in customer offices, etc., maintenance contracts between customers and image forming device manufacturers and sales companies, etc., include regular cleaning and replacement of consumable parts by maintenance personnel. Maintenance work and countermeasures in the event of an abnormality are generally performed.

  The image forming apparatus also includes parts that are consumed in relation to time, such as a roller and roller for paper conveyance, a photosensitive drum, and a cleaning roller for the photosensitive drum. For example, a roller or a roller for transporting a sheet contacts the sheet and transports the sheet, so that it is gradually consumed over a long period of use. For this reason, in order to guarantee stable operation of the image forming apparatus, the maintenance staff replaces these consumable parts at a predetermined timing. In this case, for example, the replacement time of various consumable parts is determined based on the number of sheets formed by the customer's image forming apparatus and the usage time, and the visit time of the maintenance staff is determined. In this way, manufacturing and sales companies of image forming apparatuses, in order to keep the image forming apparatus used by the customer in good condition, make a maintenance plan to prevent failure, and maintenance personnel visit the customer. In general, maintenance work is performed.

  That is, (1) maintenance work for preventive maintenance in which various consumable parts are periodically replaced based on various number of used sheets and time (referred to as “periodic preventive maintenance" in this specification), and (2) regular Regular consumable parts replacement is not involved, but in order to keep the equipment used by customers in good condition, regular cleaning and adjustment of the inside and outside of the equipment (referred to as “periodic confirmation maintenance maintenance” in this specification) Are done regularly. Furthermore, (3) maintenance work for repairing troubles that can be repaired only by maintenance personnel as reported by the customer (referred to as “emergency fault repair maintenance” in this specification), (4) support for using the device, etc. Maintenance work not corresponding to the above (1) to (3) (referred to as “other maintenance” in this specification) is performed by maintenance personnel of a maintenance company (manufacturing company, sales company, etc.).

In general, maintenance work for such devices as image forming apparatuses by customer visits of maintenance personnel is planned so that customers in neighboring areas can be visited together in order to visit customers efficiently. . Various systems have been developed to support such maintenance work by maintenance personnel (see, for example, Patent Document 1). This patent document 1 discloses a system that notifies the latest maintenance management status of an information communication terminal to an arbitrary contact point set in advance and provides a highly convenient maintenance management service. In addition, a system for predicting failure of an image forming apparatus has been developed (see, for example, Patent Document 2). This patent document 2 discloses a system that can predict until the occurrence time of an abnormal state such as a failure in an image forming apparatus.
JP 2003-50882 (page 4-10) Japanese Patent Laying-Open No. 2005-17874 (page 6-20)

However, as described above, even if maintenance work is performed on a device such as an image forming apparatus according to a maintenance plan for preventing failure, a failure may occur. In such a case, the work of the device on which the failure has occurred Should be given priority. For this reason, in addition to the place of visit during the maintenance plan, it is necessary to visit the place of installation of the device in which the failure has occurred. In particular, in an area where the maintenance management range is wide, it takes a long time to travel, which is inefficient. Further, for example, if a device failure occurs in the vicinity of the customer who performed the maintenance work on the previous day, the travel time will be longer than when the maintenance work is performed on the previous day.

  The present invention has been made in order to solve the above-mentioned problems, and the purpose of the present invention is to provide a failure of a device when a maintenance person visits the installation location of the device and performs maintenance work on the device based on a visit plan. An object of the present invention is to provide a maintenance support method and a maintenance support program for preventing the occurrence of the failure and efficiently maintaining the equipment.

  In order to solve the above problems, the invention according to claim 1 supports maintenance of equipment using equipment information storage means for recording equipment information including equipment identification information and location information, and a computer. A maintenance support method, wherein the computer specifies a target device for normal maintenance work specified on a scheduled visit date in a visit plan in which identification information of the target device for normal maintenance work and a planned visit date are associated with each other. , A visitor location specifying step for specifying location information of a visit destination from the device information storage means based on the identification information of the target device, and a neighboring installation device installed in a predetermined range from the visit destination, the device information storage A neighboring location device identifying stage that is identified by means, a device status obtaining stage that obtains status information related to the status of the neighboring installed device, and the neighboring installed device using the status information. Making predictions diagnostic possibilities failure Te, and summarized in that to perform the failure prediction diagnosis step of outputting the predicted diagnostic results.

  According to a second aspect of the present invention, in the maintenance support method according to the first aspect, the neighboring location device specifying step is based on a distance from a position of a target device for normal maintenance work in the visit plan. The gist is to identify the devices located in the vicinity of.

  The invention according to claim 3 is the maintenance support method according to claim 1 or 2, wherein the prediction diagnosis includes a total period of a number of days from a current date to the scheduled visit date and a designated number of days for failure prediction diagnosis, Using the state information, the gist is to perform the possibility of the occurrence of a failure within the total period for the neighboring equipment.

  According to a fourth aspect of the present invention, in the maintenance support method according to any one of the first to third aspects, the device state acquisition step acquires a plurality of types of information related to the state of the device, and the failure prediction diagnosis The step of determining, for each device, a method for calculating an index value based on information acquired continuously for each day of a period corresponding to the total number of days during normal operation of the device for the plurality of types of information A step of calculating an index value by the calculation method using a plurality of types of information acquired in the device status acquisition step, and a period from the current date to the time after the total number of days based on the calculated index value And a step of determining a change in the state of the device in the network.

The invention according to claim 5 is the maintenance support method according to any one of claims 1 to 4, wherein the setting information can be arbitrarily set using an input means.
According to a sixth aspect of the present invention, in the maintenance support method according to any one of the first to fifth aspects, the device is an image forming apparatus.

The invention according to claim 7 is a maintenance support program for supporting maintenance of a device by using a device information storage means that records device information including device identification information and location information, and a computer. In the visit plan in which the identification information of the target device for normal maintenance work and the scheduled visit date are associated with each other, the computer identifies the target device for normal maintenance work on the specified planned visit date, and identifies the target device. The location information specifying means for specifying the location information of the visited location from the device information storage means based on the location, and the neighborhood location using the device information storage means to specify the neighboring installed device installed within a predetermined range from the visited location Using the state information specifying means, the state information acquisition means for acquiring the state information related to the state of the neighboring installed device, and the state information, a failure may occur in the neighboring installed device. Making predictions diagnosis of sex, and summarized in that function as failure prediction diagnosis means outputs the predictive diagnosis.

  According to an eighth aspect of the present invention, in the maintenance support program according to the seventh aspect of the present invention, the neighboring location device specifying means is configured to make the visited site based on a distance from a position of a target device for normal maintenance work in the visit plan. The gist is to make it function as a means for specifying a device located in the vicinity of the device.

  The invention according to claim 9 is the maintenance support program according to claim 7 or 8, wherein the prediction diagnosis includes a total period of the number of days from the current date to the scheduled visit date and the designated number of days for failure prediction diagnosis, Using the state information, the gist is to perform the possibility of the occurrence of a failure within the total period for the neighboring equipment.

  The invention according to claim 10 is the maintenance support program according to any one of claims 7 to 9, wherein the device status acquisition means functions as a means for acquiring a plurality of types of information related to the status of the device, The failure prediction and diagnosis means, for each device, for the plurality of types of information, based on the information obtained continuously for each day of the period corresponding to the total number of days during normal operation of the device, the index value calculation method Determining means for each device using a plurality of types of information acquired by the device status acquisition means, and calculating the index value calculated for each device. Based on the above, the gist is to function as a means for determining a change in the state of the device in the period from the current date to the total number of days.

The gist of the invention described in claim 11 is that, in the maintenance support program according to any one of claims 7 to 10, the setting information can be arbitrarily set using an input means.
The invention according to claim 12 is the maintenance support program according to any one of claims 7 to 11, wherein the device is an image forming apparatus.

(Function)
According to the first or seventh aspect of the invention, the computer performs the normal maintenance work of the specified scheduled visit date in the visit plan in which the identification information of the target device for the normal maintenance work and the scheduled visit date are associated with each other. The target device is specified, and the location information of the visited place is specified from the device information storage means based on the identification information of the target device. Then, a nearby installed device installed within a predetermined range from the visited site is identified using the device information storage means, and status information regarding the status of the nearby installed device is acquired. Then, the state information is used to perform a predictive diagnosis of the possibility of the occurrence of a failure for the neighboring equipment, and the prediction diagnosis result is output. Based on the result of failure prediction diagnosis for the equipment installed in the predetermined range from the target equipment in the visit plan, whether or not the equipment installed in the predetermined range from the target equipment in the visit plan is subject to maintenance work Can be determined. Therefore, it is possible to add, as a maintenance work target, a device that is installed in a predetermined range from the target device in the visit plan and has a high possibility of failure. For this reason, it is possible to efficiently perform maintenance work without taking the time required for the maintenance personnel to move as compared with the case where the nearby installed device fails later.

  According to the second or eighth aspect of the invention, the device located in the vicinity of the visited site is specified based on the distance from the position of the target device for the normal maintenance work in the visit plan. For this reason, it is possible to determine an additional maintenance work target device from the devices installed within a predetermined distance from the position of the target maintenance target device. Thus, since it is within the range of the predetermined distance, the travel time can be limited, and it is possible to efficiently visit the installation location of the equipment and perform the maintenance work.

According to the invention described in claim 3 or 9, the predictive diagnosis is performed by using the total period of the number of days from the current date to the scheduled visit date and the designated number of days for failure prediction diagnosis, and the state information. Regarding the installed equipment, the possibility of occurrence of a failure within the total period is performed. For this reason, it is possible to set a device that may fail during the period up to the total number of days as a maintenance work target.

  According to invention of Claim 4 or 10, the multiple types of information regarding the state of an apparatus is acquired. For each type of information, an index value calculation method is determined based on information acquired during a period corresponding to the total number of days during normal operation of the device. Then, using the obtained plural types of information, the index value is calculated by the determined calculation method, and based on the calculated index value, the change in the state of the device during the period from the current date to the total number of days Determine. For this reason, it is possible to individually determine the possibility that the device will fail within the period from the current date to the total number of days based on the state during normal operation for each device and the current state. Therefore, it is possible to perform failure prediction diagnosis within a period from the current date to the time after the total number of days based on the current state by using data on the state in each normal operation for each device, and failure prediction for each device. Can improve the accuracy.

  According to invention of Claim 5 or 11, setting information can be set arbitrarily. For example, it is possible to flexibly cope with the situation by arbitrarily setting information for specifying the neighborhood range of the visited site as the setting information or failure prediction diagnosis designated days.

  According to the invention described in claim 6 or 12, the device can be an image forming apparatus.

  According to the present invention, when a maintenance person visits an installation location of a device based on a visit plan and performs maintenance work on the device, the occurrence of a failure of the device is prevented in advance and the device is efficiently maintained. be able to.

  Hereinafter, an embodiment embodying the present invention will be described with reference to FIGS. In the present embodiment, when a maintenance staff visits an installation location of equipment based on a visit plan and performs equipment maintenance work, it is possible to prevent the occurrence of equipment failure and to efficiently maintain equipment. The maintenance support method and the maintenance support program will be described. In the present embodiment, in maintenance of an image forming apparatus as a device, when a maintenance person visits the installation location of the device and performs maintenance work based on the visit plan, a predetermined range from the installation location of the target machine in the visit plan Failure prediction diagnosis is performed on the devices installed in the vicinity (neighboring devices), and devices that are likely to fail within a predetermined period are added as maintenance work targets.

  As shown in FIG. 1, the failure prediction diagnosis maintenance support system 20 is connected to an advance maintenance maintenance planning system 50. Further, the failure prediction diagnosis maintenance support system 20 is connected to the image forming apparatus 70 via the public line 60. Note that the connection between the failure prediction diagnosis maintenance support system 20 and the image forming apparatus 70 may be via a network other than the public line 60 such as a LAN.

  The pre-maintenance maintenance plan planning system 50 performs a process for planning a pre-maintenance plan, and outputs visit plan data and a visit plan list for each maintenance staff. In the present embodiment, a visit plan for each maintenance staff is devised for (1) periodic preventive maintenance and (2) regular confirmation maintenance. The prior maintenance maintenance plan planning system 50 outputs a visit plan list and transfers the visit plan data to the failure prediction diagnosis maintenance support system 20.

The failure prediction diagnosis maintenance support system 20 includes a management computer 21. The management computer 21 includes control means (CPU), storage means (RAM, ROM, hard disk, etc.), data transmission / reception means, etc., not shown. The management computer 21 executes processing to be described later regarding maintenance support. The management computer 21 includes a condition input / reception unit 31, an information decomposition unit 32, a device position information search / extraction unit 33, an in-range device information search / extraction unit 34, a failure prediction diagnosis request unit 35, a failure prediction diagnosis unit 36, a device A state information acquisition unit 37 and a failure prediction result information output unit 38 are provided.

  The condition input / reception unit 31 (1) Model / machine number information of the work target device being planned for visit, (2) Scheduled visit date information, (3) Failure prediction within m days (range days) from the planned visit date Input regarding diagnosis designated days information (range date information of failure prediction diagnosis) and (4) range distance information (for example, within 1 km) of failure prediction diagnosis is accepted.

  The information decomposing unit 32 uses the information received by the condition input / accepting unit 31 as (1) information (model / machine number information) for extracting device position information and (2) information used for a failure prediction diagnosis request ( Failure prediction diagnosis range distance, scheduled visit date, failure prediction diagnosis range days).

  The device position information search / extraction unit 33 searches and extracts the device position information in the device information storage unit 41 based on the model / machine number information. That is, the device location information search / extraction unit 33 functions as a visited location specifying unit described in the claims.

  The in-range device information search / extraction unit 34 searches for and extracts device information within the range distance (for example, within 1 km) of the failure prediction diagnosis based on this position information. That is, the in-range device information searching / extracting unit 34 functions as a nearby location device specifying unit described in the claims.

  The failure prediction diagnosis requesting unit 35 performs the failure prediction diagnosis based on the device information extracted by the in-range device information search / extraction unit 34, the scheduled visit date information, and the range days information of the failure prediction diagnosis. Ask 36.

  The failure prediction diagnosis unit 36 performs failure prediction diagnosis for the device requested for diagnosis using the device state information acquired from the device state information acquisition unit 37 and a diagnosis parameter group described later. That is, the failure prediction diagnosis unit 36 functions as failure prediction diagnosis means described in the claims.

The device state information acquisition unit 37 acquires the state information of the target device. That is, the device state information acquisition unit 37 functions as a device state acquisition unit described in the claims.
The information output unit 38 outputs the result of failure prediction diagnosis.

The management computer 21 is connected to a device information storage unit 41, a device state history storage unit 42, and a diagnostic parameter information storage unit 43 as device information storage means.
As shown in FIG. 2, the device information storage unit 41 stores device data 410 related to the image forming apparatus 70 to be maintained. The device data 410 is recorded when input registration is performed on the image forming apparatus 70 based on a maintenance contract. In the present embodiment, the device data 410 includes data related to a model identifier, a machine number, address information, position information, and other various information.

  In the model identifier data area, data relating to an identifier for specifying the model of the image forming apparatus 70 is recorded. In the machine number data area, data relating to the machine number (machine number) of the image forming apparatus 70 is recorded. In the present embodiment, the image forming apparatus 70 is specified by the model identifier and the machine number.

In the address information data area, data relating to the address of the installation place of the image forming apparatus 70 is recorded.
In the position information data area, data relating to latitude and longitude is recorded as position information for specifying the installation location of the image forming apparatus 70.

In the various information data areas, data relating to various information about the image forming apparatus 70 and the installation location of the image forming apparatus 70 such as a telephone number and an IP address are recorded.
As shown in FIG. 3, the device state history storage unit 42 stores device state history data 420 relating to the state of the image forming apparatus 70 to be maintained. The device status history data 420 is recorded when the device status of the image forming apparatus 70 is acquired. In the present embodiment, the device status history data 420 includes data relating to the model identifier, machine number, device status acquisition date, item type, and status.

  In the model identifier data area, data relating to a model identifier for specifying the model of the image forming apparatus 70 is recorded. Data relating to the machine number (machine number) of the image forming apparatus 70 is recorded in the machine number data area.

In the device status acquisition date / time data area, data related to the date / time (year / month / day / hour / minute / second) of acquiring the device status of the image forming apparatus 70 is recorded.
In the item type data area, data related to the item type of the acquired device state is recorded. Specifically, data relating to item types such as sensing information (a), control parameter information (b), and input image information (c) described later is recorded.

  In the status data area, data related to the device status acquired for this item type is recorded. Specifically, a numerical value acquired for this item type is recorded. Details of each item type are disclosed in Patent Document 2.

  As shown in FIG. 4, the diagnostic parameter information storage unit 43 stores diagnostic parameter related data 430 relating to a diagnostic parameter group used for failure prediction of the image forming apparatus 70. This diagnostic parameter related data 430 is recorded when a diagnostic parameter group is formed in accordance with the processing described later. When diagnostic parameter related data 430 of the same model identifier, machine number, and number of diagnosis requests is recorded, it is updated with a newly formed diagnostic parameter group. In the present embodiment, the diagnostic parameter related data 430 includes data relating to a model identifier, a machine number, the number of diagnosis request days, and a diagnostic parameter group.

  In the model identifier data area, data relating to a model identifier for specifying the model of the image forming apparatus 70 is recorded. Data relating to the machine number (machine number) of the image forming apparatus 70 is recorded in the machine number data area.

In the diagnosis request days data area, data related to the diagnosis request days for failure diagnosis, which is calculated according to the processing described later, is recorded.
In the diagnostic parameter group data area, data relating to a diagnostic parameter group calculated in accordance with processing to be described later is recorded. The diagnostic parameter group is calculated based on data acquired in a normal state. Here, the normal state means a case in which no problem has occurred continuously during the diagnosis request days, and data acquired on each day between the diagnosis request days in the normal state is used.

The image forming apparatus 70 includes an image forming unit 71. The image forming unit 71 includes various units (a printer unit, a paper feeding unit, a scanner unit, a document conveying unit, and the like) for performing image formation. The image forming unit 71 is provided with a state information acquisition unit 73 for each of a plurality of parts 72. Each part 72 is a part from which the device state information is acquired, and is, for example, a photosensitive drum or a drive motor. The state information acquisition unit 73 acquires state information about each part 72. For example, status information about the photosensitive drum is acquired by detecting the rotational speed of the photosensitive drum with an encoder. Moreover, the drive motor status information is acquired by acquiring the current value and temperature of the drive motor. Regarding the acquisition of the device state information in the image forming apparatus 70, the state information acquisition unit 73 acquires the state information of each part 72 by the acquisition method disclosed in Patent Document 2 in detail. Each state information acquisition unit 73 is connected to a communication unit 74 provided in the image forming apparatus 70. The communication unit 74 is connected to the management computer 21 of the failure prediction diagnosis maintenance support system 20 via a LAN adapter (not shown) and the public line 60.

  Next, using the system configured as described above, failure prediction diagnosis is performed for devices installed within a predetermined range from the visited site during the visit plan, and equipment that is likely to fail within a predetermined period is maintained. A processing procedure in the case of a work target will be described. In the present embodiment, based on the visit plan for the maintenance work of the image forming apparatus 70 that is the target of the regular maintenance work (“periodic preventive maintenance maintenance”, “periodic confirmation maintenance maintenance”), the image formation in this visit plan is performed. Failure prediction diagnosis is performed on a device (image forming device 70) installed in a predetermined range from the device 70, and a device with a high possibility of failure is added as a maintenance work target within a predetermined period.

(Acquisition of device status information)
In the present embodiment, device state information acquired by the device state information acquisition unit 37 is used in failure prediction diagnosis. This device status information will be described. The device state information acquisition unit 37 acquires the state information of each part 72 acquired by each state information acquisition unit 73 of the image forming apparatus 70 for each image forming apparatus 70.

The device state information acquired by the device state information acquisition unit 37 is information such as sensing information (a), control parameter information (b), and input image information (c).
The sensing information is information including data obtained by various sensors provided in or around the image forming apparatus. The sensing information includes the dimensions of each part of the apparatus, the speed of the moving body in the apparatus, time (timing), weight, current value, potential, vibration, sound, magnetic force, light quantity, temperature, humidity, and the like.

  The control parameter information is generally information stored as a result of device control. The control parameter information includes user operation history, power consumption, toner consumption, various image forming condition setting history, warning history, and the like.

  The input image information is obtained from information input to the image forming apparatus 70 as image data. The input image information includes the cumulative number of colored pixels, character portion ratio, halftone portion ratio, color character ratio, toner consumption distribution in the main scanning direction, RGB signal (total toner amount in pixel units), document size, and document with borders. , Character type (size, font), etc.

The specific information types of these sensing information (a), control parameter information (b), and input image information (c) and the acquisition method thereof are described in Patent Document 2.
The device status information acquisition unit 37 acquires device status information by: (1) The device status information acquisition unit 37 transmits a device status information acquisition request to the image forming apparatus 70, and the device status information is acquired from the image forming apparatus 70. And (2) a method of receiving an automatic report transmitted from the communication unit 74 of the image forming apparatus 70.

In addition, the device status information acquired in each image forming apparatus 70 may be stored in the failure prediction diagnosis maintenance support system 20, or the device status information stored in each image forming device 70 may be used in failure prediction diagnosis. You may get it. In the present embodiment, the device state information acquisition unit 37 acquires the device state information acquired in each image forming apparatus 70 and stores it in the device state history storage unit 42. In the present embodiment, the state information of each part 72 is acquired at least once a day by each state information acquisition unit 73, and the state information of each part 72 by each state information acquisition unit 73 in the same image forming apparatus 70. Is acquired at almost the same timing.

(Maintenance support by failure prediction diagnosis)
Next, a visit plan is prepared for each maintenance staff, and a failure prediction diagnosis is performed on the equipment installed within a predetermined range from the visit destination on the planned visit date in the visit plan, and a failure occurs within a predetermined period. A process for adding a highly probable device as a maintenance work target will be described.

  In the present embodiment, a visitor maintenance plan is prepared by the advance maintenance maintenance planning system 50. Specifically, the advance maintenance / maintenance plan planning system 50 outputs a prior maintenance / maintenance plan list for periodic maintenance / inspection work for each maintenance person every month. In this pre-maintenance maintenance plan list, the image forming apparatuses 70 subject to maintenance work are arranged according to the area name of the address of the installation location, and the model name, the machine number, and the installation location address are printed for each image forming apparatus 70. ing.

  Each maintenance person makes a visit plan so that the image forming apparatus 70 included in the pre-maintenance maintenance plan list can be a maintenance work target so that the installation place of the image forming apparatus 70 can be efficiently visited. At this time, the same area is grouped together to reduce the travel time of maintenance personnel. Each maintenance staff decides a scheduled visit date for each image forming apparatus 70 to be maintained in the list of his / her pre-maintenance maintenance plan and inputs it to the pre-maintenance maintenance plan planning system 50. Based on this, the prior maintenance maintenance planning system 50 outputs a visit plan list in which the image forming apparatuses 70 subject to the maintenance work are rearranged in order of the visit schedule date for each maintenance staff.

  In this visit plan list, a visit date, a model name, a machine number, and an address of an installation place of the image forming apparatus 70 are printed. Furthermore, the advance maintenance maintenance plan planning system 50 transfers the visit plan data corresponding to the visit plan list to the failure prediction diagnosis maintenance support system 20. In this visit plan data, data related to the maintenance staff identifier, scheduled visit date, model information, and machine number are recorded. The failure prediction diagnosis maintenance support system 20 that has received this visit plan data records this visit plan data in a visit plan data storage unit (not shown).

  The visit planning may be made automatically by the advance maintenance / maintenance planning system 50 using the location information of the installation location of the image forming apparatus 70 and information on the location area. In this case, the same visit plan list and visit plan data are output for each maintenance staff.

(Failure prediction diagnosis for equipment installed within a predetermined range from the visited site during the visit plan)
The maintenance staff visits the customer for the maintenance work of the image forming apparatus 70 that is the maintenance work target, based on the output visit plan list. At this time, with respect to equipment (image forming apparatus 70) installed within a predetermined range from the visited site, based on the result of failure prediction diagnosis described later, a device that is highly likely to fail within a predetermined period is subject to maintenance work. And The failure prediction diagnosis for the devices installed within a predetermined range from the visited site may be performed at any time after the visit planning and before the visit. For example, (1) the regular maintenance visit (“regular preventive maintenance maintenance”, “periodic confirmation maintenance maintenance”) may be performed on the day or a predetermined number of days before (for example, the previous day), or (2) when the visit plan list is output, You may continue. Further, (3) at the time of emergency failure repair and maintenance, it may be performed for devices installed within a predetermined range from the visited site.

In the present embodiment, each maintenance staff performs failure prediction diagnosis for the devices installed in a predetermined range from the visited site for all scheduled visit dates in the visited plan list after outputting the visited plan list. Do. Based on this result, a device that is likely to fail within a predetermined period is added as a maintenance work target. Furthermore, each maintenance staff performs failure prediction diagnosis on the equipment installed within a predetermined range from the visit destination on the scheduled visit date on the day of the planned visit date or a predetermined number of days before (for example, the previous day) in the visit plan, Devices that are likely to fail within a predetermined period are added as maintenance targets for the scheduled visit date.

  Here, as will be described later, a distance (range distance for failure prediction diagnosis) for specifying the range of the installation location of the device subject to failure prediction diagnosis and a failure within m days (range days) from the scheduled visit date Predictive diagnosis designated days (failure predictive diagnosis range days) can be arbitrarily set by user (maintenance staff, etc.) input. For example, when making a failure prediction diagnosis for a device installed in a predetermined range from a visited place when making a visit plan, the range distance of failure prediction diagnosis and the range days of failure prediction diagnosis are as follows: Similar values may be used. Further, different range distances and range days may be designated for each scheduled visit date. In the present embodiment, the number of days from the planned visit date to the next scheduled date for visiting the same region is used as the range of days for failure prediction diagnosis.

  Hereinafter, a processing procedure in the case of performing a failure prediction diagnosis for a device installed within a predetermined range from a visited site during a visit plan by the failure prediction diagnosis maintenance support system 20 will be described with reference to FIGS.

  The maintenance staff inputs the visit plan condition and the failure prediction diagnosis request condition at the input means of the failure prediction diagnosis maintenance support system 20. More specifically, the scheduled visit date, the model / machine number information (model identifier / machine number) of the maintenance target on this scheduled visit date, the range days of failure prediction diagnosis, and the range distance of failure prediction diagnosis are input. The condition input / reception unit 31 of the failure prediction diagnosis maintenance support system 20 receives this input (step S1-1), and transfers the input data to the information decomposition unit 32. A maintenance staff identifier may be input instead of the model / machine number information. In this case, the condition input / reception unit 31 extracts the model / machine number information of the visit plan data recorded in the visit plan data storage unit based on the maintenance staff identifier and the scheduled visit date.

  The information decomposing unit 32 decomposes the input data related to the visit planning condition and the failure prediction diagnosis request condition into data of model / machine number information, failure prediction diagnosis range distance, scheduled visit date, and failure prediction diagnosis range days. Then, it is recorded in the memory in the management computer 21 (step S1-2).

  Then, the information decomposing unit 32 transfers the model / machine number information to the device location information search / extraction unit 33, transfers the data related to the range distance of the failure prediction diagnosis to the in-range device information search / extraction unit 34, and plans to visit Data relating to the date and the number of days of failure prediction diagnosis is transferred to the failure prediction diagnosis requesting unit 35. Based on the model / machine number information (machine number and model information) transferred from the device location information search / extraction unit 33, the device location information search / extraction unit 33 stores the image of the maintenance work target from the device information storage unit 41. The position information of the forming apparatus 70 is extracted (step S1-3). Then, the device location information search / extraction unit 33 transfers the extracted location information to the in-range device information search / extraction unit 34.

  The in-range device information search / extraction unit 34 calculates the position information of the failure prediction diagnosis based on the position information of the device and the range distance of the failure prediction diagnosis (step S1-4). Specifically, the target of failure prediction diagnosis based on the position information (latitude and longitude) transferred from the device position information search / extraction unit 33 and the range distance of failure prediction diagnosis transferred from the information decomposition unit 32 The position information (latitude and longitude) of the range is calculated. At this time, when a plurality of model / machine number information is input as the image forming apparatus 70 to be maintained on the scheduled visit date, the position of each of the plurality of image forming apparatuses 70 and the range of failure prediction diagnosis Based on the distance, position information (latitude and longitude) of the target range for failure prediction diagnosis is calculated.

Then, the in-range device information search / extraction unit 34 searches for and extracts the target model / machine number information of the failure prediction diagnosis within the position information range of the failure prediction diagnosis (step S1-5). Specifically, based on the calculated position information (latitude and longitude) of the target range of failure prediction diagnosis, the position information in the device information storage unit 41 is searched, and the image forming apparatus included in the target range of failure prediction diagnosis 70 device data 410 are extracted. As a result, the device data 410 for the image forming apparatus 70 having the specified range distance (range distance for failure prediction diagnosis) from the installation location of any of the image forming apparatuses 70 to be maintained on the designated scheduled visit date is obtained. Extracted. Then, the in-range device information searching / extracting unit 34 transfers the model / machine number information (model identifier / machine number) of the extracted device data 410 to the failure prediction diagnosis requesting unit 35.

  The failure prediction diagnosis request unit 35 that has received this model / machine number information calculates the number of diagnosis request days for failure prediction diagnosis based on the scheduled visit date and the range of failure prediction diagnosis previously received from the information decomposition unit 32. (Step S1-6). Specifically, first, the current date is acquired, and the number of days (n days) from the current date to the scheduled visit date is calculated. Then, the total number of days (n + m days) between the number of days (n days) from the current date to the scheduled visit date and the range days (m days) of the failure prediction diagnosis previously received from the information decomposing unit 32 is calculated. Is the number of diagnosis request days for failure prediction diagnosis. That is, the failure prediction diagnosis indicates the possibility that a failure will occur between the current day and the scheduled visit date (n days) and the total number of days (n + m days) after the failure prediction diagnosis range days (m days). set to target. Here, the range days (m days) of failure prediction diagnosis is a period from the scheduled visit date to the end date of the target of failure prediction diagnosis. For this reason, when processing by the failure prediction diagnosis maintenance support system 20 is performed for the same scheduled visit date, if there is no change in the end date of the target of failure prediction diagnosis, the range days of failure prediction diagnosis regardless of the execution time of the processing (M days) is the same number of days.

Then, the failure prediction diagnosis requesting unit 35 transfers the model / machine number information received from the in-range device information search / extraction unit 34 and the number of days of diagnosis request for failure prediction diagnosis to the failure prediction diagnosis unit 36.
The failure prediction diagnosis part 36 which received this implements failure prediction diagnosis (step S1-7). This process will be described with reference to FIGS.

(Diagnosis parameter group formation)
The failure prediction diagnosis unit 36 performs failure prediction diagnosis of the image forming apparatus 70 using the device state history data 420 acquired by the device state information acquisition unit 37 and accumulated in the device state history storage unit 42.

  First, the failure prediction diagnosis unit 36 determines the number of diagnosis request days (n + m days) for failure prediction diagnosis for the image forming apparatus 70 identified by the model / machine number information received from the in-range device information search / extraction unit 34. A diagnosis parameter group is formed from the acquired device state information, assuming that a problem does not occur continuously as a “normal state”. This will be described below.

  Here, a case where no problem occurs continuously during the diagnosis request days (n + m days) of the failure prediction diagnosis is defined as “normal state”. In the “normal state”, the combination pattern of the acquired device states is defined. Then, the state deviating from this is regarded as an abnormal state, that is, a (potential) failure state.

  Prior to the calculation of the index value, the calculation method of the index value is determined. In the present embodiment, the index value is calculated as a distance in the multidimensional space by defining a multidimensional space in which different coordinate axes are set for each of a plurality of pieces of input information. For this purpose, in a state where the image forming apparatus 70 is operating normally, a plurality of types of information related to the state of the image forming apparatus 70 are obtained (sequentially for n + m days) corresponding to the number of diagnosis request days (n + m days). .

Hereinafter, the processing procedure for determining the index value calculation method (forming the diagnostic parameter group) will be described with reference to FIG.
As shown in FIG. 6, first, k pieces of information that are considered to be related to the state of the image forming apparatus 70 are acquired while operating the image forming apparatus 70 (n + m days) (step S2-1). ). Information acquisition is as described above, and a specific example thereof will be described later.

Table 1 shows the data structure of the acquired information (in the case of n sets). K pieces of data are obtained under the first condition (the first day). Let them be y ₁₁ , y ₁₂ ,..., Y _1k . Similarly, the data obtained under the following condition (second day) is y ₂₁ , y ₂₂ ,..., Y _2k , etc., and n + m sets (n + m days worth) of data are acquired.

次に、情報の種類（ｊ）ごとに、式（１）を用いて生データ（例えばｙ_ij）を平均値（ｙ_j）と標準偏差（σ_j）で規格化する（ステップＳ２−２）。表２は、表１に示すデータを数１に示す式を用いて規格化した結果を示している。

Next, for each type of information (j), the raw data (for example, y _ij ) is normalized by the average value (y _j ) and the standard deviation (σ _j ) using the equation (1) (step S2-2). . Table 2 shows the result of normalizing the data shown in Table 1 using the equation shown in Equation 1.

このステップＳ２−１、ステップＳ２−２の処理を、すべてのｉ，ｊについて終了するまで（ステップＳ２−３においてＹＥＳになるまで）繰り返す。そして、すべてのｉ，ｊについて終了した場合（ステップＳ２−３においてＹＥＳの場合）、各規格化情報の相関係数ｒ_pqを算出する（ステップＳ２−４）。すなわち、数２で示す式を用いてｋ種類のうち２組のデータ間の相関係数ｒ_pq（＝ｒ_qp）をすべて求め、それらを数３で示すように行列Ｒで表わす。

The processes in steps S2-1 and S2-2 are repeated until all i and j are completed (YES in step S2-3). When the processing is completed for all i and j (YES in step S2-3), the correlation coefficient r _pq of each normalized information is calculated (step S2-4). That is, using the equation shown in Equation 2, all correlation coefficients r _pq (= r _qp ) between two sets of data among the k types are obtained, and these are represented by the matrix R as shown in Equation 3.

そして、故障内容に応じて行列要素を選択する（ステップＳ２−５）。具体的には、ｋ個の項目の中から、判定しようとする故障内容（例えば紙詰まり、濃度低下、雑音発生など）に応じてｍ個を選択する。選択のしかたは、予め決められていて、診断する故障の種類（故障内容）と必要な情報の項目の対応情報が、故障予測診断部３６内の項目指定部（図示せず）に格納されている。故障内容を絞らない場合はｍ＝ｋとなる。

Then, a matrix element is selected according to the failure content (step S2-5). Specifically, from the k items, m items are selected according to the content of the failure to be determined (for example, a paper jam, density reduction, noise generation, etc.). The selection method is determined in advance, and correspondence information between the type of failure to be diagnosed (failure content) and the necessary information items is stored in an item designation unit (not shown) in the failure prediction diagnosis unit 36. Yes. If the details of the failure are not narrowed down, m = k.

  In the present embodiment, the type of failure to be diagnosed can be specified by user input. In the present embodiment, first, failure prediction diagnosis is performed without narrowing down the failure content, and after confirming the result, the user designates failure content and performs failure prediction diagnosis for the failure content.

Then, the cofactor matrix or the inverse matrix is obtained, and the result is expressed by the parameter matrix A (a _ij ) as shown in Equation 4 (step S2-6). In addition, “Σ” in the expression shown in Equation 2 represents the sum total regarding the subscript i.

以上により、上記単一の指標値の算出するときに用いる算出式における算出パラメータの値（パラメータ行列Ａ）が定まる。ここで扱うデータ群はいずれも正常な状態を表すものであるので、取りこんだ様々な情報間に一定の相関があると考える。正常な状態から離れて故障などの異常が起こりそうになると、これらの相関に乱れが生じて、上記定義した多次元空間における原点（安定状態の平均）からの「距離」が大きくなる。この「距離」が指標値である。

As described above, the value of the calculation parameter (parameter matrix A) in the calculation formula used when calculating the single index value is determined. Since the data group handled here represents a normal state, it is considered that there is a certain correlation between the various pieces of information captured. When an abnormality such as a failure is likely to occur away from the normal state, these correlations are disturbed, and the “distance” from the origin (average of the steady state) in the multidimensional space defined above increases. This “distance” is an index value.

Then, this parameter matrix A (diagnostic parameter group) is written in the storage medium (step S2-7). Specifically, it is recorded in the diagnostic parameter information storage unit 43.
(Failure prediction status judgment)
Next, a case where an index value is calculated using the diagnostic parameter group and failure prediction diagnosis is performed will be described with reference to FIG.

As illustrated in FIG. 7, the failure prediction diagnosis unit 36 acquires the same k types of data (device state information) acquired in the normal state (step S3-1). Specifically, it is acquired from the image forming apparatus 70 that is the target of failure prediction diagnosis via the device state information acquisition unit 37. The latest device status information acquired by the device status information acquisition unit 37 and recorded in the device status history storage unit 42 (for example, the current day) may be acquired from the device status history storage unit 42.

  Then, the failure prediction diagnosis unit 36 reads the diagnosis parameter group stored in the diagnosis parameter information storage unit 43 (step S3-2). Specifically, a diagnostic parameter group is extracted from the diagnostic parameter information storage unit 43 based on the model identifier, the machine number, and the number of diagnosis request days. And failure prediction diagnostic part 36 computes an index value using the above-mentioned diagnostic parameter group based on a plurality of acquired information (Step S3-3).

The procedure for calculating the index value will be described with reference to FIG.
As shown in FIG. 8, k types of data x ₁ , x ₂ ,..., X _k in an arbitrary state are acquired (step S4-1). The data types correspond to y ₁₁ , y _{12 ,.} Next, number 5
The data of the acquired information is normalized using the equation shown in (Step S4-2).

ここで、規格化したデータをＸ₁,Ｘ₂,・・・,Ｘ_kとする。これをすべての種類について（全jについて）終了するまで（ステップＳ４−３においてＹＥＳとなるまで）、jを順次変化させながら繰り返す。

Here, the normalized data X _1, X _2, · · ·, and X _k. This is repeated while changing j sequentially until it is finished for all types (for all j) (YES in step S4-3).

Then, already by the calculation expressions shown in Equation 6 decided by using the elements of the inverse matrix A seeking to calculate the index value D ² (step S4-4).

この指標値Ｄ²の平方根である指標値Ｄは「マハラノビスの距離」と呼ばれている。ま
た、数６で示す式中の「Σ」は、添字ｐおよびｑに関する総和を表している。

The index value D which is the square root of the index value D ² is called “Mahalanobis distance”. In addition, “Σ” in the expression shown in Equation 6 represents the sum total regarding the subscripts p and q.

  In the state deviating from the normal state, the pattern of the input data is different from the pattern of the input data in the normal state, and the degree of deviating from the normal state is reflected in the magnitude of the index value D. In this example, “normal” means that there is no failure continuously until the number of diagnosis request days (n + m days) for failure prediction diagnosis. Therefore, when the index value D increases, it is within the number of diagnosis request days (n + m days) for failure prediction diagnosis. It is shown that a failure is likely to occur.

  When the index value is calculated in this way, the failure prediction diagnosis unit 36 outputs the result of the failure prediction diagnosis of the image forming apparatus 70 based on the obtained index value (step S3-4). Specifically, based on the magnitude of the index value D, the possibility of a failure occurring within the period of the number of diagnosis request days (n + m days) for failure prediction diagnosis is determined, and this determination result is output.

The result information of the failure prediction diagnosis is input to the failure prediction result information output unit 38 (step S1-8).
Then, until all the model / machine number information passed from the in-range device information search / extraction unit 34 is completed, that is, until all the target models / machine numbers for failure prediction diagnosis are completed (YES in step S1-9). )), The process of step S1-7 to step S1-9 is repeated.

If all the target models / machine numbers for failure prediction diagnosis are completed (YES in step S1-9), it is checked whether there are more target models / machine numbers for the visit plan (step S1-10). If there are more target models / machine numbers (NO in step S1-10), the next target model / machine number is specified, the process returns to step S1-3, and the processes of steps S1-3 to S1-10 are performed. When the target model / machine number of the visit plan is completed (in the case of YES in step S1-10), the failure prediction diagnosis unit 36 uses the failure prediction result information output unit 38 to check for a failure. Predictive diagnosis result information is output (step S1-11).

Based on the failure prediction diagnosis result information that has been output, the maintenance staff targets devices that are highly likely to fail within a predetermined period of time.
Here, as a result of examining the output failure prediction diagnosis result information, when performing failure prediction diagnosis by specifying the failure content, the user (maintenance staff, etc.) inputs the specification of the failure content. In this case, the failure prediction diagnosis unit 36 performs failure prediction diagnosis using an item corresponding to the specified failure content (failure type). That is, when the failure content is designated, the item type of the device state is selected according to the failure content. Specifically, as described above, the correspondence between the type of failure to be diagnosed stored in the above-described item designating unit and the items of necessary information in accordance with the failure content to be determined from among the k items Using the information, the device type item type (m) is selected. Then, when forming the diagnostic parameter group, the matrix element is selected according to the failure content, and the index value is based on the acquired data according to the failure content using the correlation inverse matrix A calculated based on the matrix element. Is calculated and the determination result is output.

  In addition, when performing failure prediction diagnosis by specifying failure details after performing failure prediction diagnosis without narrowing down the failure content, model / machine number information based on the results of failure prediction diagnosis performed without narrowing down the failure content The failure prediction diagnosis may be performed by specifying the failure content and specifying the failure content. Similarly to the case of performing failure prediction diagnosis without narrowing down the failure content, the processing of the above steps S1-1 to S1-11 is performed, and the failure content is specified for all the devices searched by the location information. Then, failure prediction diagnosis may be performed.

  In addition, when this failure diagnosis prediction is performed for equipment installed within a predetermined range from the visited site during the visit plan when the visit plan list is output, all scheduled visit dates during the planned visit are within the specified range from the visited site. After performing the failure prediction diagnosis of the installed equipment, the visit plan may be revised based on the result.

  In addition, when this failure prediction diagnosis is performed for a device installed within a predetermined range from the visit destination on the scheduled visit date on the scheduled visit date or a predetermined number of days before the planned visit date (for example, the previous day) Among devices that are highly likely to fail, devices having a higher possibility may be added as maintenance work targets. Also in this case, the visit plan may be revised based on the result of the failure prediction diagnosis.

As described above, according to the present embodiment, the following effects can be obtained.
In the above embodiment, the management computer 21 specifies the target device for the normal maintenance work on the specified planned visit date in the visit plan in which the identification information of the target device for the normal maintenance work is associated with the planned visit date. Then, based on the identification information of the target device, the location information of the visited place is specified from the device information storage unit 41. And the nearby installation apparatus installed in the predetermined range from the visit destination is specified using the apparatus information storage part 41, and the status information regarding the state of this adjacent installation apparatus is acquired. Then, using the total period (n + m days) of the number of days from the current date to the scheduled visit date (n days) and the number of days of failure prediction diagnosis (m days), and the equipment status information, Predictive diagnosis of the possibility of failure occurrence is performed within the total period from the current date to the total number of days, and the prediction diagnosis result is output. As a result, it is possible to add, as a maintenance work target, a device that is likely to cause a failure among devices installed within a predetermined range from the visited site in the visit plan. For this reason, it is possible to efficiently perform maintenance work without taking the time required for the maintenance personnel to move as compared with the case where the nearby installed device fails later. In addition, this total is used to diagnose the possibility of failure in the period from the current day to the planned visit date (n days) and the total number of days (n + m days) of the failure prediction diagnosis range days (m days). Equipment that may fail during the period up to the number of days later can be targeted for maintenance work.

  For this reason, for example, if a period until the next equipment in the same region is set as the maintenance work target after the scheduled visit date is set as the failure prediction period, the next time the equipment in the same area is set as the maintenance work target Equipment that may fail can be the target of maintenance work. Therefore, the possibility of a failure occurring in a device in this region can be reduced until the device in the same region is subjected to maintenance work next time, and the maintenance operation can be performed efficiently. In addition, since equipment for additional maintenance work is selected from neighboring equipment, the travel time when visiting the installation location of the equipment for additional maintenance work can be limited, and the equipment installation location can be efficiently It is possible to perform maintenance work by visiting.

  In the above embodiment, the management computer 21 selects a device (image forming device 70) located in the vicinity of the visited location based on the distance from the position of the target device (image forming device 70) for normal maintenance work in the visit plan. Identify. For this reason, it is possible to determine an additional maintenance work target device from the devices installed within a predetermined distance from the position of the target maintenance target device. Thus, since it is within the range of the predetermined distance, the travel time can be limited, and it is possible to efficiently visit the installation location of the equipment and perform the maintenance work.

  In the above embodiment, the management computer 21 acquires a plurality of types of information related to the state of the device (image forming apparatus 70). Then, for each type of information, for each type of information, a diagnostic parameter for calculating an index value based on information obtained during a period corresponding to the number of diagnosis request days (n + m days) during normal operation of the device Forming a group, calculating an index value using the acquired plural types of information and the diagnostic parameter group, and based on the calculated index value, within a period from the current date to the number of diagnosis request days (n + m days) Determine changes in device status. For this reason, it is possible to individually determine the possibility that a device will fail within the period from the current date to the number of diagnosis request days (n + m days) based on the state during normal operation for each device and the current state. Therefore, it is possible to perform failure prediction diagnosis within a period from the current date to the number of diagnosis request days (n + m days) based on the current state, using data on the state of each normal operation for each device. The accuracy of each failure prediction can be increased.

In the above embodiment, the range distance for failure prediction diagnosis and the number of days for failure prediction diagnosis can be arbitrarily set. For this reason, it can respond flexibly according to the situation.
In the above embodiment, when specifying the failure content, an item is selected from the acquired items according to the failure content to be determined, and the parameter matrix is obtained. For this reason, it is possible to make a determination using only necessary information items in accordance with the failure content to be determined.

  In the above embodiment, by performing failure prediction diagnosis without narrowing down the failure content, it is possible to determine when there is a possibility that some failure may occur although the failure content is unknown. Also, by performing failure prediction diagnosis by designating the failure content, the failure prediction diagnosis can be performed more accurately for the designated failure content.

In the above embodiment, the failure prediction diagnosis for the devices installed within a predetermined range from the visit destination based on the visit plan may be performed at any time from the visit plan to the visit. For this reason, failure prediction diagnosis can be performed flexibly. In addition, at the time of visit planning, by performing failure prediction diagnosis for the equipment installed in a predetermined range from the visited place, maintenance personnel in advance also about equipment that is likely to fail by a predetermined number of days, You can schedule a visit. In addition, by performing this on the scheduled visit date or a predetermined number of days ago, it is possible to obtain a more accurate failure prediction diagnosis result.

In addition, you may change the said embodiment into the following aspects.
In the above embodiment, the diagnostic parameter group is obtained based on the normal state of the device that is the target of the failure prediction diagnosis, but the diagnosis parameter group may not be obtained in the device that is the target of the failure prediction diagnosis. Good. For example, the diagnostic parameter group may be determined in advance based on the result of an experiment using another device or the result obtained by numerical simulation. The diagnostic parameter group may be updated periodically or at a specific time. Also, these updates may be performed at the request of the user.

  -In the above embodiment, a visit plan for regular maintenance work is prepared, and a failure prediction diagnosis is performed on a device installed within a predetermined range from the visited site in this visit plan, and a failure may occur within a predetermined period. Equipment with high performance was the subject of maintenance work. Instead of this, in the first visit plan, the maintenance target device may be determined based on the possibility of failure. For example, a device having a possibility that a failure may occur within a predetermined period is a predetermined value or more. In addition, the first visit plan to be planned may include a regular maintenance work and a maintenance work of equipment determined based on the possibility of failure. Even in such a case, as in the case of the above-described embodiment, the visit plan is to visit neighboring areas together on the same day in order to reduce the travel distance time of maintenance personnel and A failure prediction diagnosis is performed on a device installed in a nearby area, and a device having a high possibility of a failure occurring within a predetermined period is targeted for maintenance work. In this case, the schedule of the visit plan for a predetermined period (for example, one month) is determined so that the visit is made in order from the area where the equipment that is likely to fail at an earlier time exists. You may stand up.

  In the above embodiment, the user (maintenance staff or the like) examined additional maintenance work targets based on the output predicted diagnosis result information. Alternatively, the computer may determine an additional maintenance work target using the predicted diagnosis result information.

  In the above embodiment, the user (maintenance staff, etc.) arbitrarily set the range distance for failure prediction diagnosis and the range days for failure prediction diagnosis. Instead, preset values may be used for the range distance of failure prediction diagnosis and the range days of failure prediction diagnosis. The range of days for failure prediction diagnosis may be set to a value that is automatically calculated based on the period until the next visit to a neighboring area. In addition, during the period of one visit plan, the range distance of failure prediction diagnosis is automatically set so that all local devices are subject to failure prediction diagnosis. You may make it change automatically.

  In the above embodiment, the failure prediction diagnosis target device installed in a predetermined range from the visited site is within the range of the failure prediction diagnosis range distance designated from the position of the target device to be visited. Instead of this, the device within a predetermined range from the visited site may be specified for each region segment (for example, administrative district), and the device located in the same region segment as the visited site may be the target of the failure prediction diagnosis. In this case, failure prediction diagnosis may be performed by specifying the target of failure prediction diagnosis collectively for each region.

In the above embodiment, the failure prediction diagnosis is performed for each type of information using the normal state data of the diagnosis request days (n + m days) calculated for a plurality of types of information. The correlation coefficient is obtained for all combinations, the inverse matrix of the matrix whose elements are selected according to the failure content is obtained, and the period until the number of days after the diagnosis request with all elements of this inverse matrix as the diagnostic parameter group This was used to calculate an index value for determining the possibility of failure in
The method for determining the index value calculation method is not limited to this. Any method can be used to determine the index value calculation method as long as the possibility of failure occurring in the period up to the number of days after the diagnosis request is determined. Good. The determination of the possibility of failure in the period up to the number of days after the diagnosis request date is not limited to this method, and any method can be used as long as the possibility of occurrence of the failure in the period up to the number of days after the diagnosis request date can be determined. Even if it exists, it is applicable to this invention.

  In the above embodiment, the management computer 21 calculates a diagnostic parameter group for each image forming apparatus 70 and calculates an index value using the diagnostic parameter group to perform failure prediction diagnosis. The calculation of the diagnostic parameter group and the index value may be performed by each image forming apparatus 70.

  In this case, the image forming apparatus 70 may calculate a diagnostic parameter group and an index value for each of a plurality of diagnosis days, and transmit all the index values calculated for each diagnosis day to the management computer 21. Further, the index value may be determined in the image forming apparatus 70, and the diagnosis days and the calculated index value may be transmitted to the management computer 21 when necessary based on the determination result. In this case, the management computer 21 records the received diagnosis days and index values in association with the model / machine number information, and uses this data in failure prediction diagnosis for devices installed in a predetermined range from the visited site. Specifically, as in the above embodiment, after specifying a device installed in a predetermined range from the visited site, the index value is determined by the corresponding diagnosis days based on the diagnosis request days (n + m days) for the specified devices. To extract. Then, a failure prediction diagnosis is performed using this index value.

  In addition, the management computer 21 transmits data related to the number of diagnosis request days (n + m days) to a device installed within a predetermined range from the visited site, and based on this, the image forming apparatus 70 calculates a diagnosis parameter group and an index value. May be. Then, the image forming apparatus 70 transmits the calculated index value or a determination result based on the index value to the management computer 21. In this case, the management computer 21 uses this data in the failure prediction diagnosis of the equipment installed in a predetermined range from the visited site. Specifically, failure prediction diagnosis is performed based on the received index value or the determination result based on the index value for devices installed in a predetermined range from the visited site.

  In the above embodiment, the management computer 21 diagnoses the possibility that a failure will occur in the period up to the number of diagnosis request days (n + m days), but the management computer 21 predicts the failure without using the diagnosis request days (n + m days). A diagnosis may be made. In this case, for example, the management computer 21 may display the transition of the state of the device and the transition of the possibility of failure using a graph or the like. Further, the management computer 21 may output the date and time when the possibility that a failure will occur reaches a predetermined value. In this case, with reference to the output result, the user can determine whether or not the nearby installation device is a maintenance work visit target.

  In the above-described embodiment, the image forming apparatus is targeted. However, the present invention may be applied to any device as long as it is a device that is a target of visit maintenance by a maintenance staff.

1 is a schematic diagram of a system according to an embodiment of the present invention. Explanatory drawing of the data recorded on the apparatus information storage part. Explanatory drawing of the data recorded on the apparatus state log | history memory | storage part. Explanatory drawing of the data recorded on the diagnostic parameter memory | storage part. Explanatory drawing of the process sequence of one Embodiment of this invention. Explanatory drawing of the process sequence of diagnostic parameter group formation. Explanatory drawing of the process sequence of failure prediction determination. Explanatory drawing of the process sequence of calculation of an index value.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 20 ... Failure prediction diagnosis maintenance support system, 21 ... Management computer, 41 ... Equipment information storage part, 42 ... Equipment state history storage part, 43 ... Diagnosis parameter information storage part, 70 ... Image forming apparatus.

Claims (12)

A maintenance support method for supporting maintenance of equipment using equipment information storage means that records equipment information including equipment identification information and location information, and a computer,
The computer is
In the visit plan in which the identification information of the target device for the normal maintenance work and the planned visit date are associated, the target device for the normal maintenance work on the specified planned visit date is specified, and the device is based on the identification information of the target device. A visitor location identifying stage for identifying location information of the visited site from the information storage means;
A neighboring device specifying stage for specifying a neighboring device installed in a predetermined range from the visited site using the device information storage means;
A device status acquisition stage for acquiring status information regarding the status of the neighboring installed devices;
A maintenance support method, comprising: performing a predictive diagnosis of a possibility of occurrence of a failure on the neighboring equipment using the state information, and executing a failure prediction diagnosis step of outputting the prediction diagnosis result. The neighborhood location device identification step includes:
The maintenance support method according to claim 1, wherein a device located in the vicinity of the visited site is specified based on a distance from a position of a target device for normal maintenance work in the visit plan. The predictive diagnosis is:
Using the total period of the number of days from the current date to the scheduled visit date and the designated number of days for failure prediction diagnosis, and the status information, the possibility of the occurrence of a failure within the total period for the neighboring equipment is performed. The maintenance support method according to claim 1 or 2. The device status acquisition step acquires a plurality of types of information related to the device status,
In the failure prediction diagnosis stage, for each device,
For the plurality of types of information, determining a method for calculating an index value based on information obtained continuously for each day in a period corresponding to the total number of days during normal operation of the device;
Using the plurality of types of information acquired in the device status acquisition step, calculating an index value by the calculation method;
The step of determining, based on the calculated index value, a change in the state of the device within a period from the current date to the time after the total number of days is performed. The maintenance support method described in 1.   The maintenance support method according to claim 1, wherein the setting information can be arbitrarily set using an input unit.   The maintenance support method according to claim 1, wherein the device is an image forming apparatus. A maintenance support program for supporting maintenance of equipment using equipment information storage means that records equipment information including equipment identification information and location information, and a computer,
The computer,
In the visit plan in which the identification information of the target device for the normal maintenance work and the planned visit date are associated, the target device for the normal maintenance work on the specified planned visit date is specified, and the device is based on the identification information of the target device. A visited location specifying means for specifying location information of the visited location from the information storage means;
Neighboring equipment specifying means for specifying neighboring equipment installed in a predetermined range from the visited site using the equipment information storage means;
Device status acquisition means for acquiring status information regarding the status of the neighboring installed devices;
A maintenance support program that functions as a failure prediction diagnosis unit that performs a prediction diagnosis of the possibility of failure of the neighboring equipment using the state information and outputs the prediction diagnosis result. The neighboring location device specifying means,
8. The maintenance support program according to claim 7, wherein the maintenance support program functions as means for specifying a device located in the vicinity of the visited site based on a distance from a position of a target device for normal maintenance work in the visit plan. The predictive diagnosis is:
Using the total period of the number of days from the current date to the scheduled visit date and the designated number of days for failure prediction diagnosis, and the status information, the possibility of the occurrence of a failure within the total period for the neighboring equipment is performed. The maintenance support program according to claim 7 or 8. The device status acquisition means functions as a means for acquiring a plurality of types of information related to the status of the device,
The failure prediction diagnosis means,
For each device, for the plurality of types of information, a means for determining a method for calculating an index value based on information obtained continuously for each day in a period corresponding to the total number of days during normal operation of the device;
Means for calculating an index value by the calculation method using a plurality of types of information acquired by the device status acquisition means for each device;
10. The device according to claim 7, wherein each device is caused to function as a unit that determines a change in a state of a device within a period from the current date to the time after the total number of days based on the calculated index value. The maintenance support program according to any one of the above.   The maintenance support program according to any one of claims 7 to 10, wherein the setting information can be arbitrarily set using an input unit.   12. The maintenance support program according to claim 7, wherein the device is an image forming apparatus.

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