Failure prediction presentation system and failure prediction presentation method

Abstract

To quickly present knowledge of both hardware and software about a failure when the failure occurs.SOLUTION: A failure prediction presentation system 1 includes a stratification unit 2 which stratifies information related to software and information related to hardware of a facility extracted from a facility-related document in accordance with the thought of maintenance, a failure prediction AI3 which predicts a failure by learning a stratification result of the stratification unit, and a presentation unit 7 which presents a prediction result of the failure prediction AI.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a failure prediction presentation system and a failure prediction presentation method. [Background technology]

[0002] For example, in order to avoid a decrease in productivity due to malfunctions of equipment, etc., there is a demand for maintenance measures in the event of equipment failure (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-8675 A Summary of the Invention [Problem to be solved by the invention]

[0004] As a method for responding to equipment failures, there is a method for investigating the cause of the failure by comparing the failure phenomenon with hardware and software drawings. However, with the above method, it is not possible to identify the cause of the failure unless knowledge of both hardware and software is available. For these reasons, it is necessary to quickly present knowledge of both hardware and software related to the failure when it occurs.

[0005] The present invention has been made in consideration of the above-mentioned circumstances, and its object is to provide a failure prediction presentation system and a failure prediction presentation method that can quickly present both hardware and software knowledge related to a failure when a failure occurs. [Means for solving the problem]

[0006] According to the invention described in claim 1, the system includes a stratification unit (2) that stratifies information related to the software and information related to the hardware of the equipment extracted from equipment-related documents according to a maintenance philosophy, a failure prediction AI (3) that learns the stratification results by the stratification unit and predicts failures, and a presentation unit (7) that presents the prediction results by the failure prediction AI.

[0007] The system stratifies the software-related information and hardware-related information of equipment extracted from equipment-related documents according to maintenance thinking, learns the results of that stratification to predict failures, and presents the results of that prediction.By learning the results of stratification of information extracted from equipment-related documents according to maintenance thinking and predicting failures, it is possible to promptly present both hardware and software knowledge related to the failure when it occurs. [Brief description of the drawings]

[0008] [Figure 1] A functional block diagram showing the overall configuration of one embodiment. [Diagram 2] Diagram explaining the process of failure prediction [Diagram 3] Diagram explaining the process of failure analysis [Figure 4] FIG. 1 is a diagram for explaining an aspect of extracting a maintenance history. [Diagram 5] FIG. 1 is a diagram for explaining an aspect of extracting treatment candidates; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] An embodiment will be described below with reference to the drawings. As shown in FIG. 1, the failure prediction presentation system 1 is a system for predicting failures of equipment, for example. The failure prediction presentation system 1 includes a stratification unit 2, a failure prediction AI (Artificial Intelligence) 3, a maintenance history database (DB) 4, a first text analysis AI 5, a second text analysis AI 6, and a display unit 7 (corresponding to a presentation unit). The stratification unit 2, the failure prediction AI 3, the maintenance history DB 4, the first text analysis AI 5, and the second text analysis AI 6 function as a control unit of the failure prediction presentation system 1, and are mainly composed of a microcomputer. The control unit of the failure prediction presentation system 1 realizes a failure prediction presentation method. The AIs used in the failure prediction AI 3, the first text analysis AI 5, and the second text analysis AI 6 perform information processing using algorithms that mimic human intelligence, and perform processes such as learning, prediction, and extraction.

[0010] The classification unit 2 reads equipment-related documents such as operation diagrams, timing charts, equipment 3D (three-dimensional) drawings, equipment wiring diagrams, spare parts lists, change point management, process FMEA (Failure Mode and Effects Analysis), and problem countermeasure lists by image recognition processing, OCR processing, etc. The equipment-related documents include several drawings and tables containing information related to the equipment's software and information related to the hardware.

[0011] The operation diagram shows the operation of the equipment, and includes information such as sensor data names and flow charts. The timing chart includes information showing the operation of the equipment in chronological order. The equipment 3D drawing includes information showing part types and part positions. The equipment wiring diagram includes information showing the electrical wiring. The spare parts list includes information showing the frequency of part replacement. The change point management includes information on the frequency of part replacement and maintenance status. The process FMEA includes information showing expected malfunctions in the process. The problem countermeasures table includes information showing countermeasures for problems in the process.

[0012] When the stratification unit 2 reads the equipment-related documents, it extracts information from the written information of the equipment-related documents read by the equipment-related documents using information extraction software, and stratifies the sensor data name, the faulty part name, and the abnormality code name from the extracted information. The faulty part name includes information indicating the part to which the sensor data indicated by the sensor data name is related and the part that causes the failure. The abnormality code name may be called a failure mode, and includes information indicating the abnormality code induced by the faulty part indicated by the faulty part name. The stratification unit 2 rearranges the information linked by keyword reference in the order of the sensor data name, the faulty part name, and the abnormality code name, and stratifies it according to the maintenance thinking. The maintenance thinking is the thinking that a maintenance worker may have when performing maintenance. In other words, the stratification unit 2 stratifies the sensor data name, the faulty part name, and the abnormality code name from the information related to the software and the information related to the hardware of the equipment extracted from the equipment-related documents, following the thinking of a maintenance worker who performs maintenance, who identifies the sensor data from the sensor data name, predicts the part to which the sensor data is related and the part that causes the failure, and predicts the abnormality code induced by the faulty part.

[0013] The failure prediction AI 3 has a learning function and a prediction function. When the failure prediction AI 3 receives and acquires the stratification result by the stratification unit 2, the failure prediction AI 3 learns the acquired stratification result and predicts a failure.

[0014] The maintenance history DB4 stores maintenance history in units of phenomenon, cause, and treatment. The maintenance history stored in the maintenance history DB4 includes maintenance history corresponding to an abnormality code name, maintenance history not corresponding to an abnormality code name, and maintenance history corresponding to a faulty part name. Maintenance history corresponding to an abnormality code name is maintenance history in which an abnormality code is recorded in the phenomenon column. Maintenance history not corresponding to an abnormality code name is maintenance history in which, for example, natural language is recorded in the phenomenon column instead of an abnormality code. Maintenance history corresponding to a faulty part name is maintenance history in which a faulty part is recorded in the cause column.

[0015] The first text analysis AI5 extracts maintenance histories from the maintenance history DB4 based on the abnormality code name indicating the abnormality code to be analyzed as a process for extracting maintenance histories. The first text analysis AI5 extracts maintenance histories from the maintenance history DB4 that are similar to the abnormality code name and have a different notation recorded based on the similarity of the recorded contents of the entire text as maintenance histories that are the same as or similar to the abnormality code.

[0016] The second text analysis AI6, as a process for extracting treatment candidates, inputs the maintenance history extracted from the maintenance history DB4 based on the abnormality code name indicating the abnormality code to be the subject of the failure analysis or the faulty part name indicating the faulty part, and extracts treatment candidates for the abnormality code or the faulty part from the maintenance history DB4 based on the recorded contents of the treatment in the input maintenance history.

[0017] The display unit 7 displays the abnormal code prediction result and the failed part prediction result as the prediction results by the failure prediction AI 3. The display unit 7 also displays the maintenance history as the extraction result by the first text analysis AI 5. Furthermore, the display unit 7 displays the candidate treatments for the abnormal code and the candidate treatments for the failed part as the extraction results by the second text analysis AI 6.

[0018] Next, the operation of the above-mentioned configuration will be described with reference to Figures 2 to 5. Here, the process flow of failure prediction and the process flow of failure analysis will be described.

[0019] (1) Flow of failure prediction process (see Figure 2) As shown in Figure 2, the stratification unit 2 reads the operation diagram and timing chart, extracts the master behavior of the sensor data from the read information using information extraction software, and stratifies the sensor data name (S1). The stratification unit 2 reads the equipment 3D drawing, extracts the positional relationships between parts and the part names from the read information using information extraction software, and stratifies the faulty part name (S2). The stratification unit 2 reads the equipment wiring diagram, extracts the electrical connection relationships between parts and the part names from the read information using information extraction software, and stratifies the faulty part name (S3).

[0020] The classification unit 2 reads the spare parts list and change point management, and extracts information on part replacement frequency and part maintenance status from the read information using information extraction software, and classifies the names of the failing parts (S4).The classification unit 2 reads the process FMEA and problem countermeasure table, and extracts parts that are likely to fail within the process from the read information, and classifies the names of the failing parts (S5).

[0021] The stratification unit 2 refers to the faulty part name by keywords (S6), and stratifies the information linked by the keyword reference according to maintenance thinking by rearranging the order of the sensor data name, the faulty part name, and the abnormality code name (S7, which corresponds to the stratification procedure), and outputs the stratification result to the failure prediction AI 3 (S8).

[0022] When the failure prediction AI 3 receives and acquires the stratification results from the stratification unit 2, it learns the acquired stratification results, predicts failures (corresponding to a failure prediction procedure), and outputs the prediction results to the display unit 7. When the display unit 7 receives the prediction results from the failure prediction AI 3, it displays the abnormality code prediction result and the failed part prediction result as the input prediction results (corresponding to a prediction result presentation procedure).

[0023] (2) Failure analysis process flow (see Figures 3 to 5) 3, the failure prediction AI 3 predicts an abnormality code name (S11), and extracts maintenance history from the maintenance history DB 4 using the predicted abnormality code name as a keyword (S12). The failure prediction AI 3 outputs the extracted maintenance history to the first text analysis AI 5 (S13).

[0024] When the first text analysis AI 5 inputs and acquires the maintenance history from the failure prediction AI 3, it extracts the maintenance history in which a different notation similar to the abnormality code name and different from the abnormality code name is recorded based on the similarity of the recorded contents of the entire text as the maintenance history identical or similar to the abnormality code (S14), and outputs the extraction result to the display unit 7. When the display unit 7 inputs the extraction result from the first text analysis AI 5, it displays the maintenance history identical or similar to the abnormality code as the input extraction result.

[0025] As shown in FIG. 4, the first text analysis AI5 extracts a maintenance history in which "abnormality code A" is recorded in the phenomenon column, identifies "a1" as the cause of the extracted maintenance history, and identifies "a111" as the treatment. The first text analysis AI5 extracts a maintenance history in which another "abnormality code A" is recorded, identifies "a2" as the cause of the extracted maintenance history, and identifies "a222" as the treatment. "a1" and "a2" are information indicating the cause of "abnormality code A", such as code information. "a111" and "a222" are information indicating the treatment for "abnormality code A", such as code information.

[0026] When the first text analysis AI5 identifies the cause and treatment for "abnormal code A", it searches for a maintenance history that matches the identified cause and treatment. In this case, the first text analysis AI5 extracts the maintenance history of "XX failure" in which "a1" is recorded in the cause column and "a111" is recorded in the treatment column, and extracts the maintenance history of "YY corrosion" in which "a2" is recorded in the cause column and "a222" is recorded in the treatment column. That is, the first text analysis AI5 extracts the maintenance history of "XX failure" and "YY corrosion" as maintenance history identical or similar to the maintenance history of "abnormal code A". The display unit 7 displays the maintenance history of "XX failure" and "YY corrosion" extracted by the first text analysis AI5 in this manner as maintenance history identical or similar to the maintenance history of "abnormal code A".

[0027] Furthermore, the failure prediction AI 3 predicts the name of the failing part (S15), and extracts the maintenance history from the maintenance history DB 4 using the predicted failing part name as a keyword (S16).

[0028] The second text analysis AI6 extracts candidate treatments for the abnormality code and the faulty part from the maintenance history DB4 based on the recorded contents of the treatment in the maintenance history (S17), and outputs the extraction result to the display unit 7. In this case, the second text analysis AI6 extracts candidate treatments for the abnormality code and the faulty part from the maintenance history DB4 based on criteria such as frequency, length of equipment downtime, or cost of treatment. When the display unit 7 receives the extraction result from the second text analysis AI6, it displays the candidate treatments for the abnormality code and the faulty part as the input extraction result.

[0029] As shown in FIG. 5, when the second text analysis AI6 uses, for example, frequency as a criterion for candidate treatments for abnormality codes and faulty parts, if the treatment "aaa1" was the most common in the past and the treatment "aaa2" was the next most common, it extracts "aaa1" as the first candidate treatment and "aaa2" as the second candidate treatment. The display unit 7 displays the candidate treatments extracted by the second text analysis AI6 in this manner, for example, using frequency as a criterion. When the second text analysis AI6 uses the length of equipment downtime as a criterion, it extracts the treatment with the longest equipment downtime in the past as the first candidate treatment, and extracts the treatment with the second longest equipment downtime as the second candidate treatment. When the second text analysis AI6 uses the cost of treatment as a criterion, it extracts the treatment with the lowest cost in the past as the first candidate treatment, and extracts the treatment with the second lowest cost as the second candidate treatment. "n1" and "n2" indicate the number of times corresponding to frequency, and "n1" is a value greater than "n2".

[0030] In the above, the maintenance history is extracted from the maintenance history DB4 using the abnormality code name as a keyword, and the maintenance history is extracted from the maintenance history DB4 using the faulty part name as a keyword, thereby extracting candidate treatments for the abnormality code and the faulty part from the maintenance history DB4. However, candidate treatments for either the abnormality code or the faulty part may be extracted from the maintenance history DB4. That is, for example, steps S15 to S16 may be omitted, and the candidate treatments for the abnormality code may be extracted from the maintenance history DB4 simply by extracting the maintenance history from the maintenance history DB4 using the abnormality code name as a keyword. For example, steps S11 to S14 may be omitted, and the candidate treatments for the faulty part may be extracted from the maintenance history DB4 simply by extracting the maintenance history from the maintenance history DB4 using the faulty part name as a keyword.

[0031] As described above, according to this embodiment, the following advantageous effects can be obtained. In the failure prediction and presentation system 1, the software-related information and hardware-related information of the equipment extracted from the equipment-related documents are stratified according to a maintenance philosophy, the stratification results are learned to predict failures, and the prediction results are displayed. By learning the stratification results obtained by stratifying the information extracted from the equipment-related documents according to a maintenance philosophy and predicting failures, it is possible to promptly and appropriately display knowledge of both the hardware and software related to the failure when it occurs.

[0032] In addition, based on the similarity of the recorded contents of the entire text, the maintenance history in which the abnormality code name is similar to the abnormality code name and a different notation is recorded is extracted from the maintenance history database as the maintenance history identical or similar to the abnormality code, and the extraction result is displayed. By extracting and displaying the maintenance history in which the abnormality code name is similar to the abnormality code name and a different notation is recorded from the maintenance history database as the maintenance history identical or similar to the abnormality code, the maintenance history to be the subject of the failure analysis can be properly extracted and displayed from the maintenance history database without any omissions.

[0033] In addition, the system acquires a maintenance history extracted from the maintenance history database based on the abnormality code name indicating the abnormality code and the faulty part name indicating the faulty part to be analyzed, and extracts candidate treatments for the abnormality code and candidate treatments for the faulty part from the maintenance history database based on the recorded contents of the treatments in the acquired maintenance history, and displays the extraction results. By extracting and displaying candidate treatments for the abnormality code and candidate treatments for the faulty part from the maintenance history database, it is possible to appropriately extract and display candidate treatments suitable for the target of the failure analysis.

[0034] In the above, in "(2) Processing flow of failure analysis", a configuration is exemplified in which the maintenance history is extracted from the maintenance history DB4 using the abnormality code name or the faulty part name predicted by the failure prediction AI 3 as a keyword, but a configuration without using the failure prediction AI 3 is also possible. In other words, the maintenance history may be extracted from the maintenance history DB4 using the abnormality code name or the faulty part predicted by a method different from the failure prediction AI 3 as a keyword.

[0035] Although the present disclosure has been described based on the embodiment, it is understood that the present disclosure is not limited to the embodiment or structure. The present disclosure also includes various modifications and modifications within the equivalent range. In addition, various combinations and forms, as well as other combinations and forms including only one element, more than one element, or less than one element, are also within the scope and concept of the present disclosure.

[0036] The control unit and the method described in the present disclosure may be realized by a special-purpose computer provided by configuring a processor and a memory programmed to execute one or more functions embodied in a computer program. Alternatively, the control unit and the method described in the present disclosure may be realized by a special-purpose computer provided by configuring a processor with one or more dedicated hardware logic circuits. Alternatively, the control unit and the method described in the present disclosure may be realized by one or more special-purpose computers configured by a combination of a processor and a memory programmed to execute one or more functions and a processor configured with one or more hardware logic circuits. Also, the computer program may be stored in a computer-readable non-transitory tangible recording medium as instructions executed by a computer. [Explanation of symbols]

[0037] In the drawing, 1 is a failure prediction presentation system, 2 is a stratification section, 3 is a failure prediction AI, 4 is a maintenance history DB, 5 is a first text analysis AI, 6 is a second text analysis AI, and 7 is a display section (presentation section).

Claims

1. a classification unit (2) for classifying information related to the software and information related to the hardware of the equipment extracted from the equipment-related documents according to a maintenance concept; A failure prediction AI (3) that learns the stratification result by the stratification unit and predicts failures; A failure prediction presentation system comprising: a presentation unit (7) that presents a prediction result by the failure prediction AI.

2. 2. The failure prediction presentation system according to claim 1, wherein the stratification unit extracts a master behavior of the sensor data based on an operation diagram and a timing chart as the facility-related documents, and stratifies the sensor data name.

3. 2. The failure prediction presentation system according to claim 1, wherein the classification unit extracts part names and positional relationships between parts based on a three-dimensional facility drawing as the facility-related document, and classifies the names of the failing parts.

4. 2. The failure prediction presentation system according to claim 1, wherein the classification unit extracts electrical connection relationships between components and component names based on an equipment wiring diagram as the equipment-related document, and classifies the names of failed components.

5. 2. The failure prediction presentation system according to claim 1, wherein the classification unit extracts information on part replacement frequency and information on part maintenance status based on a spare parts list and change point management as the equipment-related documents, and classifies the names of the failing parts.

6. 2. The failure prediction presentation system according to claim 1, wherein the classification unit extracts parts that are prone to failure within the process based on a process FMEA and a problem countermeasure table as the equipment-related documents, and classifies the names of the failing parts.

7. 2. The failure prediction presentation system according to claim 1, wherein the stratification unit refers to part names by keywords, and rearranges the information linked by the keyword reference in the order of sensor data name, faulty part name, and abnormality code name.

8. The failure prediction AI predicts abnormal codes and faulty parts, 8. The failure prediction presentation system according to claim 1, wherein the presentation unit presents a prediction result of an abnormality code or a prediction result of a failed part.

9. A classification procedure for classifying the software-related information and the hardware-related information of the equipment extracted from the equipment-related documents according to a maintenance mindset; A failure prediction step of predicting a failure by learning a result of the stratification step; A failure prediction presentation method which performs a prediction result presentation step of presenting a prediction result obtained by the failure prediction step.

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