Maintenance Management Support System

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

[Technical Field]

[0001] The present invention relates to a maintenance management support system, and more particularly to a maintenance management support system for operating devices. [Background technology]

[0002] For example, when an abnormality or malfunction (hereinafter referred to as "malfunction") occurs in industrial equipment (hereinafter referred to as "operating equipment") operating in factories or 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 on office information equipment, and copiers that copy documents, maintenance management such as repair or replacement of the malfunctioning part (hereinafter referred to as the malfunctioning part) is required.

[0003] In this maintenance management, service personnel visit the installation site of the operating equipment based on the failure data and repair or replace the failed part. However, in this maintenance management by service personnel, due to the decrease in the number of skilled service personnel in maintenance management work and the lack of technical skills of new service personnel, there are often mistakes in estimation when identifying the failed part, and the wasteful dispatch or replacement of parts that do not need to be replaced.

[0004] This has led to various issues, such as increased time spent responding to customer inquiries, increased costs for replacement parts and unnecessary replacement work, increased number of maintenance calls, a decrease in the number of operating devices each service technician is responsible for, and reduced efficiency in maintenance work.

[0005] To address these issues, for example, Japanese Patent Application Laid-Open No. 2004-265159 (Patent Document 1) proposes technology relating to a diagnostic function that collects operational information from facility equipment such as air conditioners and searches for the cause of a failure when one occurs, and a prediction function that predicts the location of a failure based on alarms and failure information output by the facility equipment. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-265159 Summary of the Invention [Problem to be solved by the invention]

[0007] In order to improve the accuracy of diagnosing faults in operating equipment and predicting where a fault will occur, it is necessary to acquire a large amount of detailed operational data (driving data) from the equipment. However, due to limitations in communication speed and data storage capacity, it is difficult to acquire a large amount of operational data from operating equipment. This has created a new issue: it is not possible to obtain sufficient accuracy in predicting where a fault will occur. This also hinders the identification of failed parts.

[0008] An object of the present invention is to provide a maintenance management support system that can improve the accuracy of predicting fault locations and accurately identify faulty parts even when it is difficult to obtain a large amount of operational data from operating equipment. [Means for solving the problem]

[0009] The present invention is a maintenance management support system comprising: a failure information input unit into which failure information is input; a parts database storing data correlating replacement parts with the number of times failure events have occurred; a failure knowledge database storing failure knowledge data correlating failure information with replacement parts; a failing part inference unit that infers a failing part based on the failure knowledge data; and an inferred failing part display unit that displays the inferred failing parts, wherein the failing part inference unit infers a plurality of failing parts associated with the failure information from the failure knowledge data based on the failure information input from the failure information input unit, and further extracts replacement parts corresponding to the inferred failing parts from the parts database and displays the extracted replacement parts in descending order of the number of times failure events have occurred on the inferred failing part display unit. [Effects of the Invention]

[0010] According to the present invention, even when it is difficult to obtain a large amount of operational data for operating equipment, it is possible to improve the accuracy of predicting the location of a failure and accurately identify a failed part. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a configuration diagram showing a configuration of a maintenance management support system according to an embodiment of the present invention; [Figure 2] FIG. 2 is an explanatory diagram showing fault knowledge data stored in a fault knowledge database; [Figure 3] 10 is a flowchart illustrating a processing flow of a first example for estimating and displaying a faulty part. [Figure 4] 10 is a flowchart illustrating a processing flow of a second example for estimating and displaying a faulty part. [Figure 5] FIG. 10 is an explanatory diagram illustrating clustering in event analysis. [Figure 6] 10 is a flowchart illustrating a first example of a processing flow for updating fault knowledge data. [Figure 7] 10 is a flowchart illustrating a second example of a processing flow for updating fault knowledge data. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. However, the present invention is not limited to the following embodiment, and various modifications and application examples within the technical concept of the present invention are also included within its scope.

[0013] Figure 1 shows the configuration of a maintenance management support system that supports the search for fault locations and identification of faulty parts in monitored operating equipment.

[0014] 1, a maintenance management support system 10 includes a monitoring and diagnosis database 12 that stores various data including sensor data, operation sequence data, and alarm data obtained from operating equipment 11, a failure knowledge database 13 that stores failure data of the operating equipment 11, a maintenance management result database 14 that stores maintenance management work result data, and a parts database 15 that stores parts data of operating equipment and data on newly replaced parts (for example, the number of parts issued). These databases are connected to each other so that they can exchange information.

[0015] The maintenance management system 10 also includes a failure information input unit 19 into which a service person or the like inputs customer data, failure data of operating equipment, operation data of operating equipment, etc. Here, the failure data is mainly used by a first failed part estimation unit (A) described later to identify a failed part, and the operation data is mainly used by a second failed part estimation unit (B) described later to identify a failed part.

[0016] The maintenance management system 10 also includes a first faulty part estimation unit (A) 16 that estimates a faulty part from the fault data input by the fault information input unit 19 and the data of each of the databases 12 to 15, a first estimated faulty part display unit (A) 20 that displays the estimation results of the first faulty part estimation unit (A) 16, and a first faulty part selection unit (A) 21 that selects a faulty part from the estimation results of the first faulty part estimation unit (A) 16.

[0017] The maintenance management system 10 also includes a second faulty part estimation unit (B) 17 that estimates a faulty part from the operation data input by the fault information input unit 19 and the data of each database 12 to 15, a second estimated faulty part display unit (B) 22 that displays the estimation results of the second faulty part estimation unit (B) 17, and a second faulty part selection unit (B) 23 that selects a faulty part from the estimation results of the second faulty part estimation unit (B) 17.

[0018] 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 the customer, and a database update unit 18 for updating the data stored in each of the databases 13 to 15. Furthermore, each of the databases 12 to 15 and each of the functional configuration units 16 to 25 are connected to each other so that they can exchange information with each other.

[0019] Each of the databases 12 to 15 can be realized using a recording means such as an HDD (Hard Disk Drive) or RAM (Radom Access Memory). Also, each of the first faulty part inferring unit (A) 16, second faulty part inferring unit (B) 17, and database updating unit 18 can be realized using a calculation means such as a CPU (Central Processing Unit) operated by a program recorded in a ROM (Read Only Memory) or RAM.

[0020] The failure information input unit 19, first presumed failed part display unit (A) 20, first presumed failed part selection unit (A) 21, second presumed failed part display unit (B) 22, second presumed failed part selection unit (B) 23, maintenance work result input unit 24, and maintenance work report output unit 25 are each realized by an input / output device such as a touch panel, a device combining a display and keyboard (for example, a personal computer, a tablet terminal, a smartphone, etc.), a printer, etc., but are not limited to these.

[0021] Next, the function of each component will be described. Although the present invention is not limited to a specific operating device, the following description will be given taking an inkjet printer as an example.

[0022] Fault data and operation data are uploaded to the monitoring and diagnostic database 12 from the operating device 11 to be monitored, in this case an inkjet printer, via a network such as the Internet. Since there are limits to the connection speed of the Internet line and the storage capacity of the monitoring and diagnostic database 12, the fault data and operation data constantly uploaded here are data that satisfy the limitations of the connection speed of the Internet line and the capacity of the monitoring and diagnostic database 12. The operation data includes data stored by the operating device, such as the operation sequence of the operating device, sensor measurement values, alerts, and errors.

[0023] The fault knowledge database 13 records, as fault knowledge data 30 (see FIG. 2), the structure of the inkjet printer, the relationship between the main components that make up the inkjet printer and functional failures, the relationship between functional failures and failure effects, the relationship between functional failures and failure modes, and the relationship between functional failures and event analysis results. Furthermore, the fault knowledge data 30 does not necessarily contain the overall fault knowledge of the inkjet printer 11 and its relationships; fault knowledge for each structure or functional part of the target knowledge may also be stored. Overall structural expansion data for the inkjet printer 11 is also recorded.

[0024] Figure 2 shows an example of failure knowledge data 30. Here, an inkjet printer is used as an example, and some of the main components of the ink circulation unit are particularly shown. The failure knowledge data 30 contains data on partial knowledge ID 31, main components 32, functional failure 33, failure effect 34, failure mode 35, and event analysis results 36, each of which is associated with the other data.

[0025] The partial knowledge ID 31 represents a name, and related data is arranged in each row accordingly. For the main parts 32, the part ID and part name are associated as a pair. For the functional failures 33, the failure ID of the functional failure and the physical state at the time of the functional failure are associated as a pair. Then, as described above, each piece of data is associated based on the functional failure 33.

[0026] The failure effect 34 is a pair of a failure ID and an effect that occurs in response to the failure. It is also possible to associate the degree of the effect on safety and the effect on operation. The failure mode 35 is a pair of a failure mode ID and a failure mode that corresponds to the cause of the failure.

[0027] The event analysis results correspond to the operation data log, and for example, for past functional failures 33, weighting values ​​are recorded according to the number of occurrences of events recorded in the operation data. An event is the smallest unit of operation sequence, sensor measurement value, alert, error, etc. saved as operation data. The event analysis results (e.g., weighting values) are updated each time. Here, the event analysis results are calculated by calculating the number of failure events that occur each time the inkjet printer is operated or per unit time, extracting characteristic events that occur frequently, assigning weighting values ​​according to the number of occurrences, and storing them in a database.

[0028] The failure knowledge data 30 does not need to be created in its entirety at once, but may be created by an expert with specialized knowledge, such as an inkjet printer service technician or designer, or may be created in parts due to additional data added in a version upgrade, etc. The status of the knowledge data associated with the creation is recorded as a partial knowledge ID 31.

[0029] The fault knowledge database 13 also stores data showing the structural expansion of the operating equipment 11, such as the main parts 32 in Fig. 2. Here, the structural expansion is shown using an inkjet printer as an example, and only some of the main parts 32 in the ink circulation section are shown.

[0030] Each row of the failure knowledge data 30 in FIG. 2 (shown as No. 1 to 14 in FIG. 2) represents a combination of a main component 32, a functional failure 33, a failure effect 34, a failure mode 35, and an event analysis result 36, which are treated as the smallest unit of failure knowledge.

[0031] In the fault knowledge database 13, the relationship between each data recorded in the fault knowledge data 30 describes, as the relationship between each data ID, which major component 32 causes the functional failure 33 that is caused by the failure effect 34.

[0032] Next, a method for estimating a faulty part using the maintenance management support system 10 will be described with reference to Figures 3 and 4. Figure 3 shows an example of estimating a faulty part using a first faulty part estimation unit (A) 16, and Figure 4 shows an example of estimating a faulty part using a second faulty part estimation unit (B) 17.

[0033] In Figure 3, the service technician first obtains failure information by interviewing the customer. This failure information can be seen as rough information on the actual phenomenon that occurred due to the failure. Note that the failure information does not include just one piece of information, but multiple different pieces of information on failure.

[0034] Then, based on the failure information obtained, the service person inputs customer information and failure information (for example, failure information such as "unable to print," "unstable printing," or "ink leaking" as shown in FIG. 2) from the failure information input unit 9 in "Step S10." When this failure information is input, the first failed part inference unit (A) 16 uses data from the failure knowledge database 13 to infer the failed part.

[0035] Specifically, in "Step S11," the fault knowledge data 30 (see FIG. 2) in the fault knowledge database 13 is used to select multiple fault effects 34 that are estimated to be highly likely to be the cause of the fault "unable to print" based on the fault information, for example, "unable to print," that was input in "Step S10." Of course, the same applies to fault information such as "unstable printing" and "ink leakage."

[0036] Once the failure effect 34 has been selected, then in "Step S12," a plurality of functional failures 33 that are estimated to have a high probability of causing the failure "unable to print" are selected from the functional failures 33 in the failure knowledge data 30. At this time, if a functional failure 33 has been input in "Step S10," the input functional failure is selected.

[0037] Once a functional failure 33 is selected, in step S13, multiple main components 32 that are highly related to the multiple functional failures 33 can be narrowed down as candidates for the failed component. In this way, it is possible to estimate multiple failed components related to the input rough failure information. In this way, through the steps up to this point, multiple main components 32 that may be experiencing a failure can be extracted as failed components.

[0038] Next, in "Step S14", the first faulty part selection unit (A) selects main parts 32 corresponding to the narrowed-down plurality of faulty parts from the main parts 32 linked to the number of times of fault occurrence stored in the parts database 15, and the first suspected faulty part display unit (A) 20 displays the selected plurality of faulty parts in descending order of the number of times of fault occurrence.

[0039] That is, the parts database 15 stores a list of the main parts that make up the inkjet printer, and each main part is associated with a dispensing number. The dispensing number is the number of replacement parts that are replaced due to a malfunction, and can therefore be regarded as the number of malfunctions. In this specification, the number of malfunction events is defined to include events that can be regarded as the number of malfunctions (for example, the number of dispensings, the frequency of malfunctions, etc.).

[0040] Therefore, when the main parts 32 corresponding to the faulty parts extracted in step S13 from the parts database 15 are selected, the number of issues (which can be considered as the number of times the fault has occurred) is linked, so it is possible to display them in order of the number of issues.

[0041] In "Step S15," main parts corresponding to the narrowed-down plurality of faulty parts are selected from the main parts associated with the number of issues stored in the maintenance result database 14, and the read-out plurality of faulty parts are displayed in the first estimated faulty part display section (A) 20 in descending order of the number of fault occurrences.

[0042] Here, the display in "Step S14" and the display in "Step S15" may be displayed by dividing the display screen of the first suspected faulty part display unit (A) 20 into two, or may be switched by a switching input from the fault information input unit 19.

[0043] With this method, a service person can identify and dispatch a plurality of suitable replacement parts from the faulty parts displayed in descending order of the number of times that the parts have failed, thereby improving the accuracy of predicting the location of the failure and enabling accurate identification of the faulty part.

[0044] Next, an example of narrowing down the faulty parts using the event analysis results by the second faulty part inferring section (B) 17 will be described.

[0045] 4, first, the service person interviews the customer to obtain fault information. Note that the fault information may include not just one piece of fault information, but multiple different pieces of fault information.

[0046] Then, based on the malfunction information heard, the service person inputs customer information and malfunction information (for example, malfunction information such as "cannot print," "printing is unstable," or "ink is leaking") from the malfunction information input unit 9 in "Step S20."

[0047] Next, in "Step S21," the fault knowledge data 30 in the fault knowledge database 13 is used to select a plurality of fault effects 34 that are estimated to be highly likely to be the cause of the fault "unable to print" based on the fault information, for example, "unable to print," that was input in "Step S20." Of course, the same applies to fault information such as "unstable printing" and "ink leakage."

[0048] Next, in "Step S22," a plurality of functional faults 33 that are estimated to have a high probability of causing the fault "unable to print" are selected from the functional faults 33 in the fault knowledge data 30. At this time, if a functional fault 33 has been input in "Step S10," the input functional fault is selected.

[0049] When functional failure 33 is selected, next, in "Step S23," operation log data of the inkjet printer and the like are input and an event analysis is performed. In the event analysis, the number of failure events occurring for each operation of the operating equipment or per unit time is calculated from the operation log data, characteristic events with a high number of failure events are extracted, and a weighting value according to the number of failure events is assigned to each characteristic event. This assigned weighting value is compared with the event analysis results in the failure knowledge database 30 as a reference value, and the closer the weighting value is to the weighting value of the current event analysis (the smaller the deviation), the more likely it is that a failure has occurred in that major component. Therefore, the closer the reference value is to the current weighting value (the smaller the deviation), the more likely it is that a failure has occurred.

[0050] Once the event analysis is complete, in step S24, the event analysis results 36 stored in the fault knowledge data 30 in the fault knowledge database 13 are matched with the current event analysis results 36, and multiple event analysis results 36 with small deviations in the weighting values ​​of both are selected.

[0051] Next, in "Step S25", a plurality of faulty parts are narrowed down based on the AND condition of the failure effect 34 obtained in "Step S21" and / or the functional failure 32 obtained in "Step S22" and the event analysis result 36 obtained in Step S24. This makes it possible to improve the accuracy of estimating the faulty part.

[0052] Once the multiple faulty parts have been narrowed down, in step S26, the narrowed down faulty parts are sorted in order of smallest deviation of the weighting values, which are the matching results of the event analysis results 36, and displayed in the second suspected faulty part display section (B) 22.

[0053] Furthermore, similar to "Step S14" in FIG. 3, in "Step S27", main parts corresponding to the narrowed-down plurality of faulty parts are selected from the main parts stored in the parts database 15 and linked with the number of occurrences of faults, and the selected plurality of faulty parts are displayed in the second suspected faulty part display section (B) 22 in descending order of the number of occurrences of faults.

[0054] Similarly, in "Step S28", main parts corresponding to the narrowed-down plurality of faulty parts are selected from the main parts associated with the number of occurrences of faults stored in the maintenance result database 14, and the selected plurality of faulty parts are displayed in the second estimated faulty part display section (B) 22 in descending order of the number of occurrences of faults.

[0055] It is also possible to omit "Step S21" and "Step S22" and identify the faulty part from the event analysis in "Step S23".

[0056] 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 suspected faulty part display section (B) into three parts, or may be switched by a switching input from the fault information input section 19.

[0057] Here, the event analysis in "Step S23" can be performed by clustering. Now, as the maintenance management support system 10 operates, multiple event analysis results 36 may be obtained as a result of the event analysis for a failure with the same main component 32, functional failure 33, and failure effect 34 recorded in the failure knowledge data 30. For this reason, the weighting values ​​of the elements Event 1 to Event n of the event analysis result 36 may differ even for the same failure.

[0058] 5, for multiple faults with the same main component 32, functional failure 33, and failure effect 34, event elements Even 1 to Event n or several event elements Event are grouped to form a cluster CL, which is then used as classified failure information CL-1 to CL-n. By comparing the event analysis result 36 obtained by the event analysis of the second faulty component inference unit (B) 17 with this clustered failure information CL and narrowing down the main components 32 that are highly related to the event analysis result, it is possible to more accurately infer the faulty component.

[0059] When the multiple faulty components suspected to be faulty are identified using the techniques shown in FIGS. 3 and 4, the service technician performs the following tasks based on this information.

[0060] The service personnel selects some faulty parts from the faulty parts displayed in the first presumed faulty part display section (A) 20 or the second presumed faulty part display section (B) 22, and dispatches them to the customer site.

[0061] Even when operation data cannot be obtained, the service person selects several faulty parts from those displayed in the first presumed faulty part display section (A) 20, receives the parts, and is dispatched to the customer site. The service person can operate the second faulty part inference section (B) by extracting operation data from the operating equipment at the customer site and inputting it into the maintenance management support system 10. This allows the service person to check the faulty parts displayed in the second presumed faulty part display section (B) 22 and select the most appropriate faulty part from the faulty parts that have been dispatched.

[0062] Furthermore, at the customer site, the defective parts can be efficiently found by investigating the parts of the actual inkjet printer in the order of the defective parts displayed in the suspected defective part display section (A) 10 or the suspected defective part display section (B) 12, thereby shortening the maintenance management work time.

[0063] After the maintenance work is completed, the service technician inputs the details of the maintenance work and the parts used using the maintenance work result input unit 24, and a maintenance work report is automatically created and output by the maintenance work report output unit 25. This eliminates the need for the service technician to return to his or her office to create a maintenance work report, thereby improving work efficiency.

[0064] Here, a first example of updating the fault knowledge data 30 by a service person will be described with reference to FIG.

[0065] When the maintenance management support system 10 is operated, there may be cases where a functional failure 33 or a failure effect 34 does not exist in the failure knowledge data 30 for the same failure of a major component 32 recorded in the failure knowledge data 30. In this case, it is important that the data update unit 18 newly adds these to the failure knowledge data 30 and updates it.

[0066] Therefore, as shown in Fig. 6, the data update unit 18 is used to input failure information in "Step S30." At this time, if there is no failure information input in the functional failure 33 or failure effect 34, "Step S31" and / or "Step S32" are executed.

[0067] If there is no fault corresponding to the fault effect 34, in "Step S31", the fault effect 34 is extracted from the fault knowledge database 13 and the preset item "Other" is selected. Similarly, if there is no fault corresponding to the functional fault 33, in "Step S32", the functional fault 33 is extracted from the fault knowledge database 13 and the preset item "Other" is selected.

[0068] When "Other" is selected for the failure effect 33 or functional failure 34, in "Step S33," functional failure data, failure effect data, failure mode data, and event analysis result data corresponding to the failure effect 33 and functional failure 34 at that time are extracted. Furthermore, in "Step S34," each of the extracted data is assigned a new failure number (No. 15 in FIG. 2), and is added to and updated in the failure knowledge data 30.

[0069] Next, a second example of updating the fault knowledge data 30 by a service person will be described with reference to FIG.

[0070] After the maintenance work is completed, the service technician inputs the details of the maintenance work and the replacement parts into the maintenance work result input unit 14. At this time, the major parts 32 presented as faulty parts by the maintenance management support system 10 may differ from the major parts 32 input by the service technician. In this case, it is necessary to update the fault knowledge data 30 by adding these parts to the data update unit 18.

[0071] Therefore, as shown in Fig. 7, the data update unit 18 is used to input work result information of the maintenance management work results in "Step S40". Next, in "Step S41", if the used (replaced) major part is the same as the estimated major part, the process goes to "END" and ends. On the other hand, if the major part is different from the estimated major part, "Step S42" is executed.

[0072] Next, in "Step S42", if the input functional failure 33 is the same as the functional failure 33 in the failure knowledge data 30, the process goes to "END" and ends. On the other hand, if the functional failure 33 is different from the functional failure 33 in the failure knowledge data 30, "Step S43" is executed.

[0073] Next, in "Step S43", if the input failure effect 34 is the same as the failure effect 34 in the failure knowledge data 30, the process goes to "END" and ends. On the other hand, if the failure effect 34 is different from the failure effect 34 in the failure knowledge data 30, "Step S44" and / or "Step S45" is executed.

[0074] If the judgment in "Step S41" to "Step S43" is "Yes" and the failure effect 34 corresponding to the input failure effect 34 is not found in the failure knowledge data 30, then in "Step S44", the failure effect 34 is extracted from the failure knowledge database 13 and the preset item "Other" is selected. Similarly, if the functional failure 33 corresponding to the input functional failure 33 is not found in the failure knowledge data 30, then in "Step S45", the functional failure 33 is extracted from the fault knowledge database 13 and the preset item "Other" is selected.

[0075] When "Other" is selected for the failure effect 33 or functional failure 34, in "Step S46", functional failure data, failure effect data, failure mode data, and event analysis result data corresponding to the failure effect 33 and functional failure 34 at that time are extracted. Furthermore, in "Step S47", each extracted data is assigned a new failure number (No. 15 in FIG. 2), and is added to and updated in the failure knowledge data 30.

[0076] As described above, the present invention is a maintenance management support system comprising a failure information input unit into which failure information is input, a parts database storing data correlating replacement parts with the number of times failure events have occurred, a failure knowledge database storing failure knowledge data correlating failure information with replacement parts, a failing part inferring unit which infers a failing part based on the failure knowledge data, and an inferred failing part display unit which displays the inferred failing parts, wherein the failing part inferring unit infers a plurality of failing parts associated with the failure information from the failure knowledge data based on the failure information input from the failure information input unit, and further extracts replacement parts corresponding to the inferred failing parts from the parts database and displays the extracted replacement parts in descending order of the number of times failure events have occurred on the inferred failing part display unit.

[0077] This makes it possible to improve the accuracy of predicting the location of a fault and accurately identify the faulty part, even when it is difficult to obtain a large amount of operational data for the operating equipment.

[0078] The present invention is not limited to the above-described embodiments, but includes various modifications. The above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace 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. It is also possible to add, delete, or replace other configurations with respect to the configuration of each embodiment. [Explanation of symbols]

[0079] 10...Maintenance management support system, 11...Monitored equipment, 12...Monitoring diagnosis database, 13...Failure knowledge database, 14...Maintenance result database, 15...Component database, 16...First failed component estimation unit (A), 17...Second failed component estimation unit (B), 18...Database update unit, 19...Failure information input unit, 20...First estimated failed component display unit (A), 21...First failed component selection unit (A), 22...Second estimated failed component display unit (B), 23...Second failed component selection unit (B), 24...Maintenance work result input unit, 25...Maintenance work report output unit, 30...Failure knowledge data, 31...Partial knowledge ID, 32...Major components, 33...Functional failure, 34...Failure effect, 35...Failure mode, 36, Event analysis results.

Claims

[Claim 1] a failure information input unit to which failure information of an operating device to be monitored is input, a parts database storing data correlating a failed part with a number of times a failure event has occurred, a failure knowledge database storing failure knowledge data correlating the failure information with the failed part, a failed part inferring unit (A) for inferring the failed part using the failure knowledge database, a failed part selecting unit (A) for extracting the number of times a failure event has occurred of the failed part using the parts database, an inferred failed part display unit (A) for displaying the inferred failed part, and a database updating unit for updating the failure knowledge data, the faulty part estimation unit (A) estimates, based on the fault information input from the fault information input unit, a plurality of faulty parts associated with the fault information from the fault knowledge database; the faulty part selection unit (A) extracts, from the part database, the fault event occurrence counts corresponding to the plurality of faulty parts estimated by the faulty part estimation unit (A), The estimated faulty part display unit (A) uses the number of times of occurrence of the faulty event extracted by the faulty part selection unit (A) to display the estimated faulty parts in descending order of the number of times of occurrence of the faulty event. A maintenance management support system characterized by:

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