JP2023113148A - Maintenance management support system - Google Patents

Abstract

To provide a maintenance management support system capable of accurately identifying a failure component by increasing prediction accuracy of a fault occurrence point even when it is difficult to acquire a large amount of operation data of an operation apparatus.SOLUTION: A maintenance management support system includes: a component database 15 that stores data in which a replacement component and a failure event occurrence frequency are associated; a failure knowledge database 13 that stores failure knowledge data in which failure information and the replacement component are associated; a first failure component estimation unit 16 and a second failure component estimation unit 17 that estimate, on the basis of the failure knowledge data, failure components; and a first estimated failure component display unit 20 and a second estimated failure component display unit 22 that display the estimated failure components. Each failure component estimation unit estimates, on the basis of the input failure information, a plurality of failure components related to the failure information from the failure knowledge data, extracts the replacement component corresponding to the estimated failure components from the component database 15, and displays on each estimated failure component display unit in descending order of the failure event occurrence frequency of the replacement component.SELECTED DRAWING: Figure 1

Description

The present invention relates to a maintenance management support system, and more particularly to a maintenance management support system for equipment in operation.

For example, factories and offices such as inkjet printers that print the manufacturing date and serial number for each product manufactured in a factory, printers that print documents created by office information equipment, and copiers that copy documents In the industrial equipment (hereinafter referred to as "operating equipment") that is operating in a factory, when an abnormality or failure (hereinafter typically referred to as "failure") occurs, the part that caused the failure (hereinafter referred to as It is necessary to perform maintenance management such as repairing or replacing parts (referred to as faulty parts).

In this maintenance management, based on failure data, a service person visits the installation location of the operating equipment and repairs or replaces the failed parts. By the way, in the maintenance management by the service personnel, the decrease in the number of skilled service personnel in the maintenance management work and the lack of technical skills of the new service personnel lead to misestimation when identifying faulty parts and parts that do not need to be replaced. Useless payouts, exchanges, and the like are often carried out.

This will increase the time to respond to customer inquiries, increase the cost of replacement parts and wasteful replacement work of replacement parts, increase the number of dispatches for maintenance management, reduce the number of operating devices that one service person is responsible for, It causes various problems such as a decrease in efficiency of maintenance and management work.

In response to such a problem, for example, in Japanese Patent Application Laid-Open No. 2004-265159 (Patent Document 1), operation information of equipment such as an air conditioner is collected, and when a failure occurs, the cause of the failure is identified. and a prediction function for predicting the location of failure based on alarms and failure information output by equipment.

JP 2004-265159 A

By the way, in order to improve the accuracy of diagnosing a failure of an operating device and improve the accuracy of predicting the failure location, it is necessary to acquire a large amount of detailed operation data (operating data) of the operating device. However, it is actually difficult to obtain a large amount of operating data of operating devices due to restrictions such as communication speed and data storage capacity. For this reason, a new problem arises that sufficient prediction accuracy of the failure occurrence location cannot be obtained. Therefore, it is difficult to identify the faulty component.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a maintenance management support system that can accurately identify a faulty part by increasing the accuracy of predicting the faulty location even when it is difficult to obtain a large amount of operational data of the operating equipment.

The present invention comprises a failure information input unit for inputting failure information, a parts database for storing data in which replacement parts are associated with the number of occurrences of failure events, and failure knowledge data in which failure information and replacement parts are associated. A maintenance management support system comprising a failure knowledge database storing a failure knowledge database, a failure part estimation unit for estimating a failure part based on the failure knowledge data, and an estimated failure part display unit for displaying the estimated failure part, wherein the failure part Based on the failure information input from the failure information input unit, the estimation unit estimates a plurality of failure parts related to the failure information from the failure knowledge data, and performs replacement corresponding to the estimated failure parts from the parts database. It is characterized in that parts are extracted and displayed on the presumed failure part display section in descending order of the number of failure event occurrences of the extracted replacement parts.

According to the present invention, even when it is difficult to obtain a large amount of operation data of operating equipment, it is possible to increase the accuracy of predicting the location of the failure and accurately identify the failure component.

1 is a configuration diagram showing the configuration of a maintenance management support system according to an embodiment of the present invention; FIG. FIG. 4 is an explanatory diagram showing failure knowledge data stored in a failure knowledge database; FIG. 10 is a flowchart for explaining a processing flow of a first example of estimating and displaying a faulty component; FIG. FIG. 11 is a flowchart for explaining a processing flow of a second example of estimating and displaying a faulty component; FIG. It is an explanatory view explaining clustering in event analysis. FIG. 10 is a flowchart for explaining a processing flow of a first example for updating failure knowledge data; FIG. FIG. 11 is a flowchart illustrating a second example of a processing flow for updating failure knowledge data; FIG.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the present invention is not limited to the following embodiments, and various modifications and applications can be made within the technical concept of the present invention. is also included in the scope.

FIG. 1 shows the configuration of a maintenance management support system that assists in searching for a faulty part of an operating device to be monitored and identifying faulty parts.

In FIG. 1, a maintenance management support system 10 includes a monitoring and diagnostic database 12 in which various data including sensor data, operation sequence data, alarm data, etc. obtained from operating equipment 11 are stored, and failure data of the operating equipment 11, etc. are stored. a maintenance management result database 14 that stores maintenance work result data, etc.; and a maintenance management result database 14 that stores parts data of operating equipment and newly replaced replacement parts data (e.g., the number of delivered parts). A parts database 15 is provided. These databases are connected so as to exchange information.

The maintenance management system 10 also includes a failure information input unit 19 for inputting customer data, failure data of operating equipment, operation data of operating equipment, etc. by service personnel or the like. Here, the failure data is mainly used to identify failure parts in the first failure part estimation unit (A) described later, and the operation data is mainly used to identify failure parts in the second failure part estimation part (B) described later. Used to identify parts.

The maintenance management system 10 also includes a first failure part estimation unit (A) 16 for estimating failure parts from the failure data input by the failure information input unit 19 and the data of each database 12 to 15, and a first failure part estimation A first estimated failure part display unit (A) 20 that displays the estimation results of (A) 16, and a first failure part selection unit (A) that selects failure parts from the estimation results of the first failure part estimation unit (A) 16 ) 21.

In addition, the maintenance management system 10 includes a second failure part estimation unit (B) 17 for estimating failure parts from the operation data input by the failure information input unit 19 and the data of the databases 12 to 15, and a second failure part estimation unit (B) 17. A second estimated faulty part display section (B) 22 that displays the estimation result of the section (B) 17, and a second faulty part selection section ( B) 23.

Furthermore, the maintenance management system 10 includes a maintenance work result input unit 24 for inputting details of maintenance work result data, a maintenance work report output unit 25 for outputting work reports to be submitted to customers, and each database 13 to 15. and a database updating unit 18 for updating the stored data. The databases 12 to 15 and the functional units 16 to 25 are connected to each other so that information can be exchanged with each other.

Each of the databases 12 to 15 can be realized using recording means such as HDD (Hard Disk Drive) or RAM (Radom Access Memory). Also, the first failure part estimation unit (A) 16, the second failure part estimation unit (B) 17, and the database updating unit 18 are operated by programs recorded in ROM (Read Only Memory) or RAM. It can be realized using a computing means such as a CPU (Central Processing Unit).

Failure information input unit 19, first estimated failure component display unit (A) 20, first failure component selection unit (A) 21, second estimated failure component display unit (B) 22, second failure component selection unit (B) 23. The maintenance work result input unit 24 and the maintenance work report output unit 25 are realized by input/output devices such as a touch panel, a device combining a display and a keyboard (for example, a personal computer, a tablet terminal, a smartphone, etc.), and a printer. , but not limited to

Next, the function of each component will be described. It should be noted that the present invention is not limited to a specific operating device, but the following description will proceed with an ink jet printer as an example.

Fault data and operation data are uploaded to the monitoring diagnostic database 12 from the operating device 11 to be monitored, here, an inkjet printer, via a network such as the Internet line. Since the connection speed of the Internet line and the storage capacity of the monitoring and diagnosis database 12 are limited, the failure data and operation data that are always uploaded here satisfy the limits of the connection speed of the Internet line and the capacity of the monitoring and diagnosis database 12. Data. Note that the operating data includes data stored by the operating device, such as the operation sequence of the operating device, sensor measurement values, alerts, and errors.

In the failure knowledge database 13, the structure of the inkjet printer, the relationship between the main parts that make up the inkjet printer and the functional failure, the relationship between the functional failure and the failure effect, the relationship between the functional failure and the failure mode, the functional failure and the event analysis result. are recorded as failure knowledge data 30 (see FIG. 2). Further, the failure knowledge data 30 does not need to include the entire failure knowledge of the inkjet printer 11 and its relationship, and there may be cases where the structure of the target knowledge or the failure knowledge for each functional part is stored. In addition, structural expansion data of the entire inkjet printer 11 is also recorded.

FIG. 2 shows an example of the failure knowledge data 30. As shown in FIG. Here, an inkjet printer is shown as an example, and in particular, some of the main parts of the ink circulation section are shown. In the failure knowledge data 30, data relating to a partial knowledge ID 31, a main component 32, a functional failure 33, a failure effect 34, a failure mode 35, and an event analysis result 36 are described in association with each row.

The partial knowledge ID 31 means naming, and associated data is arranged in each row corresponding to this. The main part 32 is associated with a part ID and a part name in pairs. The functional failure 33 is associated with a pair of the failure ID of the functional failure and the physical state when the functional failure occurred. Each data is associated based on the functional failure 33 as described above.

The fault effect 34 is associated with a pair of a fault ID and an effect that occurs corresponding to the fault. It is also possible to associate the degree of impact on safety with the degree of impact on operation. The failure mode 35 is associated with a failure mode ID and a failure mode corresponding to the cause of the failure.

The event analysis result corresponds to the operation data log. For example, in the functional failure 33 that occurred in the past, a weighted value according to the number of occurrences of the event recorded in the operation data is recorded. An event is the smallest unit of an operation sequence, sensor measurement value, alert, error, etc., saved as operation data. It should be noted that this event analysis result (for example, weighting value) is updated each time. Here, the event analysis results are obtained by calculating the number of occurrences of failure events for each operation of the inkjet printer or for each unit of time, extracting characteristic events with a large number of occurrences, assigning a weighting value according to the number of occurrences, and storing them in a database. is becoming

The failure knowledge data 30 does not need to be created in its entirety at one time, and may be created by an expert with specialized knowledge such as an inkjet printer service person or a designer, or may be partially created by adding data due to a version upgrade. It is sometimes created intentionally. The state of knowledge data associated with its creation is recorded as a partial knowledge ID 31 .

The failure knowledge database 13 also records data representing structural development of the operating equipment 11, such as the main parts 32 in FIG. Here, structural development is shown using an inkjet printer as an example, but here, a part of the main components 32 of the ink circulation section are shown.

Each row of the failure knowledge data 30 in FIG. , which are treated as the minimum unit of fault knowledge.

In the failure knowledge database 13, based on the relationship between each data recorded in the failure knowledge data 30, it is possible to identify which main component 32 causes the functional failure 33 caused by the failure effect 34 between each data ID. Described as relevance.

Next, a method of estimating a faulty component using the maintenance management support system 10 will be described with reference to FIGS. 3 and 4. FIG. FIG. 3 shows an example of estimating a faulty part using the first faulty part estimator (A) 16, and FIG. 4 shows an example of faulty part estimation using the second faulty part estimator (B) 17. For example.

In FIG. 3, first, the service person obtains failure information by interviewing the customer. This failure information can be viewed as rough failure information such as the actual phenomenon caused by the failure. It should be noted that not only one piece of failure information but also a plurality of different pieces of failure information are included.

Then, based on the hearing of the failure information, the customer information and failure information (for example, as shown in FIG. 2, "printing is not possible", "printing is unstable", etc.) , failure information such as "ink leaks") is input. When these pieces of failure information are input, the first failure component estimator (A) 16 uses the data in the failure knowledge database 13 to estimate the failure component.

Specifically, in "step S11", using the failure knowledge data 30 (see FIG. 2) of the failure knowledge database 13, based on the failure information such as "cannot print" input in "step S10", to select a plurality of failure effects 34 which are estimated to have a high possibility of occurrence of a failure of "impossible to print". Of course, the same applies to failure information such as "unstable printing" and "ink leakage".

When the failure effect 34 is selected, next in "step S12", from among the functional failures 33 of the failure knowledge data 30, a plurality of failures that are estimated to have a high possibility of "cannot print" have occurred. function failure 33 is selected. At this time, if the functional failure 33 is input in "step S10", the input functional failure is selected.

When the functional failure 33 is selected, next in "step S13", a plurality of main parts 32 highly related to the plurality of functional failures 33 can be narrowed down as failure part candidates. In this way, it is possible to estimate a plurality of faulty parts related to this fault information from the inputted rough fault information. In this way, through the steps up to this point, it is possible to extract a plurality of main parts 32 that may have failed as failed parts.

Next, in "step S14", the main parts 32 corresponding to the plurality of narrowed-down failure parts are selected from the main parts 32 linked with the number of failure occurrences stored in the parts database 15 as the first failure part selection. A plurality of selected faulty parts are displayed in the first presumed faulty part display part (A) 20 in descending order of the frequency of occurrence of faults.

In other words, the parts database 15 stores a list of main parts that make up the inkjet printer, and each main part is associated with the number of payouts. Since the number of deliveries is the number of replacement parts that are replaced due to failure, it can be regarded as the number of occurrences of failure. Thus, in this specification, the number of occurrences of failure events is defined to include events that can be regarded as the number of occurrences of failures (for example, the number of deliveries, the frequency of failures, etc.).

Therefore, when the main parts 32 corresponding to the faulty parts extracted in step S13 are selected from the parts database 15, the number of payouts (which can be regarded as the number of occurrences of failures) is associated with them. They can be displayed in order.

Also in "step S15", a main part corresponding to a plurality of narrowed down failure parts is selected from the main parts linked with the number of deliveries similarly stored in the maintenance result database 14, and the first presumed failure is selected. In the parts display section (A) 20, the read out faulty parts are displayed in descending order of the number of occurrences of faults.

Here, the display in 'step S14' and the display in 'step S15' may be displayed by dividing the display screen of the first presumed faulty component display section (A) 20 into two. The display may be switched by the switching input of .

With this method, the service personnel can identify and dispatch a plurality of appropriate replacement parts from the faulty parts displayed in the order of the frequency of occurrence of faults. identification can be performed accurately.

Next, an example of narrowing down the faulty parts using the event analysis result by the second faulty part estimator (B) 17 will be described.

In FIG. 4, the service person first obtains failure information by interviewing the customer. It should be noted that not only one piece of failure information but also a plurality of different pieces of failure information are included.

Then, based on the hearing of the failure information, the customer information and failure information (e.g., "cannot print", "unstable printing", "ink leaks") are sent from the failure information input unit 9 by the service staff in "step S20". ”) is input.

Next, in 'step S21', using the failure knowledge data 30 of the failure knowledge database 13, based on the failure information input in 'step S20', for example, 'printing is not possible', 'printing is not possible' is performed. A plurality of failure effects 34 that are highly likely to occur are selected. Of course, the same applies to failure information such as "unstable printing" and "ink leakage".

Next, in "step S22", a plurality of functional failures 33 are selected from among the functional failures 33 of the failure knowledge data 30, which are presumed to have a high possibility of "cannot print". At this time, if the functional failure 33 is input in "step S10", the input functional failure is selected.

When the functional failure 33 is selected, next in "step S23", the operation log data of the ink jet printer, etc. are input and event analysis is performed. Event analysis is performed by calculating the number of occurrences of failure events for each operation of the operating device or for each unit time from the operation log data, extracting characteristic events with many occurrences of failure events, and extracting the weighting value corresponding to the number of occurrences of failure events. is given. This given weighting value is compared with the event analysis result of the failure knowledge database 30 as a reference value. presumed to be of high quality. Therefore, it can be assumed that the closer this reference value is to the current weighted value (the smaller the deviation), the more likely a failure has occurred.

When the event analysis is completed, next in "step S24", the event analysis result 36 stored in the failure knowledge data 30 of the failure knowledge database 13 and the current event analysis result 36 are matched, and the weighting value of both is A plurality of event analysis results 36 with small deviations of are selected.

Next, in "step S25", one or both of the failure effect 34 obtained in "step S21" and the functional failure 32 obtained in "step S22" and the event analysis result 36 obtained in step S24 Narrow down multiple faulty parts from the AND condition of . This makes it possible to improve the accuracy of estimating the faulty component.

When a plurality of faulty parts are narrowed down, next in "step S26", the narrowed down faulty parts are arranged in descending order of the deviation of the weighting value, which is the matching result of the event analysis result 36, and displayed in the second estimated faulty part display section. (B) is displayed at 22.

Furthermore, in the same way as "step S14" in FIG. is selected, and a plurality of selected faulty parts are displayed in the second presumed faulty part display section (B) 22 in descending order of the number of fault occurrences.

Similarly, in "step S28", main parts corresponding to a plurality of narrowed down failure parts are selected from the main parts linked to the number of occurrences of failures stored in the maintenance result database 14, and a second estimated failure is selected. In the parts display section (B) 22, a plurality of selected faulty parts are displayed in descending order of the number of occurrences of faults.

Incidentally, it is also possible to omit "step S21" and "step S22" and specify the faulty component from the event analysis of "step S23".

The display in 'step S26', the display in 'step S27', and the display in 'step S28' may be displayed by dividing the display screen of the second presumed faulty part display section (B) into three parts, or by displaying the fault information The display may be switched by switching input from the input section 19 .

Here, the event analysis in "step S23" can perform event analysis by performing clustering. Now, by operating the maintenance management support system 10, a plurality of event analysis results 36 are obtained as a result of event analysis for the same failure recorded in the failure knowledge data 30, which has the same main component 32, functional failure 33, and failure effect 34. You may get it. Therefore, the weighting values of the elements Event 1 to Event n of the event analysis result 36 may differ even for the same failure.

Therefore, as shown in FIG. 5, in a plurality of failures having the same main component 32, functional failure 33, and failure effect 34, event elements Even 1 to Event n, or a cluster CL grouped by several event elements Event is formed. The failure information CL-1 to CL-n are formed and classified. By comparing the event analysis result 36 obtained by the event analysis of the second failure part estimation unit (B) 17 with the clustered failure information CL, and narrowing down the main parts 32 highly related to the event analysis result , a more accurate estimation of the faulty part can be made.

When a plurality of faulty parts estimated to be faulty are identified by the method shown in FIGS. 3 and 4, the service person performs the following work based on this information.

The service person selects some faulty parts from the faulty parts displayed in the first presumed faulty parts display section (A) 20 or the second presumed faulty parts display section (B) 22, and receives payment. dispatched to the customer site.

Also, even if the operation data cannot be acquired, the service staff selects some of the faulty parts displayed in the first presumed faulty parts display section (A) 20, receives payment, and dispatches to the customer's site. do. At the customer's site, the service staff can retrieve the operating data from the operating equipment and input it into the maintenance management support system 10 to operate the second failure part estimator (B). As a result, it is possible to check the faulty parts displayed in the second estimated faulty part display section (B) 22 and select a more appropriate faulty part from among the faulty parts that have been issued.

Further, at the customer site, by investigating the parts of the actual inkjet printer in the order of the failure parts displayed in the presumed failure part display section (A) 10 or the presumed failure part display section (B) 12, It is possible to efficiently find the parts that are out of order, and to shorten the maintenance work time.

After the maintenance work is completed, the service engineer inputs the details of the maintenance work and the parts used using the maintenance work result input unit 24, whereby a maintenance work report is automatically created and the maintenance work is performed. It is output by the report output unit 25 . This eliminates the need for service personnel to return to their own offices to create maintenance work reports, thereby improving work efficiency.

Here, a first example of updating the failure knowledge data 30 by a service person will be described with reference to FIG.

Due to the operation of the maintenance management support system 10 , there are cases where the failure knowledge data 30 does not contain the functional failure 33 or the failure effect 34 for the same failure of the main component 32 recorded in the failure knowledge data 30 . In this case, it is important that the data updating section 18 newly adds these to the failure knowledge data 30 and updates them.

Therefore, as shown in FIG. 6, the data updating unit 18 is used to input failure information in "step S30". At this time, if there is no failure information input to the functional failure 33 or failure effect 34, "step S31" and/or "step S32" are executed.

When there is no failure corresponding to the failure effect 34, in "step S31", the failure effect 34 is extracted from the failure knowledge database 13, and the preset "other" item is selected. Similarly, when there is no failure corresponding to the functional failure 33, in "step S32", the functional failure 33 is extracted from the failure knowledge database 13, and the preset "Others" item is selected.

When "others" of the failure effect 33 and the functional failure 34 are selected, in "step S33", the functional failure data, the failure effect data, and the failure mode data corresponding to the failure effect 33 and the functional failure 34 at this time are displayed. , to extract event analysis result data. Furthermore, each extracted data is given a new failure number (as No. 15 in FIG. 2), added to the failure knowledge data 30, and updated in "step S34".

Next, a second example of updating the failure knowledge data 30 by a service person will be described with reference to FIG.

After the maintenance work is completed, the service person inputs the details of the maintenance work and replacement parts to the maintenance work result input unit 14 . At this time, the main part 32 presented as the faulty part by the maintenance management support system 10 may be different from the main part 32 input by the service person. In this case, it is necessary to update the failure knowledge data 30 by adding them to the data update unit 18 .

Therefore, as shown in FIG. 7, the data update unit 18 is used to input work result information of maintenance management work results in "step S40". Next, in "step S41", if the used (replaced) main parts are the same as the estimated main parts, the processing is ended. On the other hand, if the main parts are different from the estimated main parts, "step S42" is executed.

Next, in "step S42", if the input functional failure 33 is the same as the functional failure 33 of the failure knowledge data 30, the processing is ended. On the other hand, if the functional failure 33 is different from the functional failure 33 of the failure knowledge data 30, "step S43" is executed.

Next, in "step S43", if the input failure effect 34 is the same as the failure effect 34 of the failure knowledge data 30, the processing is ended. On the other hand, if the fault effect 34 is different from the fault effect 34 of the fault knowledge data 30, "step S44" and/or "step S45" are executed.

If there is no failure knowledge data 30 corresponding to the failure effect 34 that has been determined as "Yes" in "steps S41" to "steps S43", the failure knowledge database 13 is selected in "step S44". Extract the influence 34 and select the preset "Other" item. Similarly, when there is no failure knowledge data 30 for the functional failure 33 corresponding to the input functional failure 33, the functional failure 33 is extracted from the failure knowledge database 13 in "step S45", and the preset "other" is selected. Select an item.

When "others" of the failure effect 33 and the functional failure 34 are selected, in "step S46", functional failure data, failure effect data, and failure mode data corresponding to the failure effect 33 and the functional failure 34 at this time are displayed. , to extract event analysis result data. Furthermore, each extracted data is given a new failure number (as No. 15 in FIG. 2), added to the failure knowledge data 30, and updated in "step S47".

As described above, the present invention includes a failure information input unit for inputting failure information, a parts database for storing data in which replacement parts are associated with the number of occurrences of failure events, and a system for relating failure information and replacement parts. A maintenance management support system comprising: a failure knowledge database storing failure knowledge data obtained from the failure knowledge data; a failure part estimation unit for estimating failure parts based on the failure knowledge data; and an estimated failure part display unit for displaying the estimated failure parts. The failure part estimator estimates a plurality of failure parts related to the failure information from the failure knowledge data based on the failure information input from the failure information input part, and further extracts the estimated failure from the parts database. It is characterized in that the replacement parts corresponding to the parts are extracted and displayed on the presumed failure part display section in descending order of the number of failure event occurrences of the extracted replacement parts.

According to this, even when it is difficult to obtain a large amount of operation data of operating equipment, it is possible to increase the accuracy of predicting the location of the failure and accurately identify the failure component.

It should be noted that the present invention is not limited to the above-described embodiments, and includes various modifications. The above embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the described configurations. Moreover, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Other configurations can be added, deleted, or replaced with respect to the configuration of each embodiment.

DESCRIPTION OF SYMBOLS 10...Maintenance management support system 11...Monitoring target equipment 12...Monitoring diagnosis database 13...Failure knowledge database 14...Maintenance result database 15...Parts database 16...First failure part estimation unit (A) 17... Second failure component estimation unit (B) 18 Database update unit 19 Failure information input unit 20 First estimated failure component display unit (A) 21 First failure component selection unit (A) 22 2nd presumed faulty part display section (B), 23... second faulty part selection section (B), 24...maintenance work result input section, 25...maintenance work report output section, 30...failure knowledge data, 31...partial knowledge ID , 32 .. main component 33 .

Claims (8)

A failure information input unit for inputting failure information of an operating device to be monitored, a parts database storing data in which replacement parts are associated with the number of occurrences of failure events, and a relationship between the failure information and the replacement parts. a failure knowledge database storing failure knowledge data obtained from the failure knowledge data; a failure part estimating unit for estimating a failure part based on the failure knowledge data; an estimated failure part display unit for displaying the estimated failure part; A maintenance management support system comprising a database update unit for updating data,
the faulty component estimating unit, based on the fault information input from the fault information input unit, estimating a plurality of the faulty components related to the fault information from the fault knowledge data;
Further, the replacement parts corresponding to the estimated failure parts are extracted from the parts database, and the extracted replacement parts are displayed on the presumed failure part display unit in descending order of the failure event occurrence frequency. Characterized maintenance management support system. The maintenance management support system according to claim 1,
comprising another faulty part estimator different from the faulty part estimator,
the other faulty part estimator estimates a plurality of the faulty parts related to the operation data from the fault knowledge data based on the operation data of the operating device input from the operating device;
Further, the replacement parts corresponding to the estimated failure parts are extracted from the parts database, and the extracted replacement parts are displayed on the presumed failure part display unit in descending order of the failure event occurrence frequency. Characterized maintenance management support system. The maintenance management support system according to claim 2,
The failure knowledge data in the failure knowledge database includes main components constituting the operating equipment, functional failures representing physical states caused by failures, failure effects representing effects caused by failures, failure modes representing causes of failures, including an event analysis result consisting of a weighted value according to the number of failure event occurrences, and these data are allocated to each of the main parts;
The faulty component estimating unit estimates the faulty component from the functional fault and the fault effect, and the other faulty component estimating unit estimates the faulty component from the event analysis result. Maintenance management support system. The maintenance management support system according to claim 3,
The maintenance management support system, wherein the other failure part estimating unit estimates the failure part that matches one or both of the event analysis result, the functional failure, and the failure effect. The maintenance management support system according to claim 3,
The result of the event analysis is obtained by calculating the number of occurrences of failure events for each operation of the operating equipment or for each unit of time, extracting characteristic events having many occurrences of failure events, and extracting the weighting value according to the number of occurrences of failure events. A maintenance management support system characterized by: The maintenance management support system according to claim 3,
When the input failure information does not exist in the functional failure or the failure effect of the failure knowledge data, the database updating unit adds the main component, the functional failure, A maintenance management support system characterized by adding and updating the failure effect, the failure mode, and the event analysis result. The maintenance management support system according to claim 3,
When a part different from the faulty part displayed in the presumed faulty part display section is used as the replacement part, the database updating unit adds the main part, the functional fault, A maintenance management support system characterized by adding and updating the failure effect, the failure mode, and the event analysis result. A maintenance management support system according to claim 5,
In the other faulty part estimation unit, a cluster is formed by grouping a plurality of the event analysis results, the current event analysis result is compared with the cluster, and the faulty part is selected from the cluster closest to the event analysis result. A maintenance management support system characterized by estimating

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