WO1995022089A1 - Procede et dispositif de diagnostics d'incidents - Google Patents

Procede et dispositif de diagnostics d'incidents Download PDF

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Publication number
WO1995022089A1
WO1995022089A1 PCT/JP1995/000171 JP9500171W WO9522089A1 WO 1995022089 A1 WO1995022089 A1 WO 1995022089A1 JP 9500171 W JP9500171 W JP 9500171W WO 9522089 A1 WO9522089 A1 WO 9522089A1
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WO
WIPO (PCT)
Prior art keywords
sensor
failure
degree
abnormality
failure diagnosis
Prior art date
Application number
PCT/JP1995/000171
Other languages
English (en)
Japanese (ja)
Inventor
Hiroyoshi Yamaguchi
Original Assignee
Komatsu Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Komatsu Ltd. filed Critical Komatsu Ltd.
Publication of WO1995022089A1 publication Critical patent/WO1995022089A1/fr

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0267—Fault communication, e.g. human machine interface [HMI]

Definitions

  • the present invention relates to a method and an apparatus for diagnosing a failure of a failure diagnosis target such as a construction machine.
  • This system asks the operator on the screen about the items to be diagnosed at the time of failure diagnosis on the screen, and the operator inspects these inspection items, and inputs the inspection results as abnormalities for each inspection item. It uses this data to perform fault diagnosis, also called an interactive diagnosis system:
  • This system captures sensor signals from sensors installed at the target of failure diagnosis at the time of failure diagnosis, calculates the degree of abnormality based on these sensor signals, and performs failure diagnosis using the calculated degree of abnormality. It is also called an automatic diagnostic system-
  • This system always captures sensor signals from sensors arranged for failure diagnosis, determines whether or not there is an abnormality based on these sensor signals, and issues a warning if it is determined to be abnormal. It emits:
  • the abnormality monitoring system described in C above simply determines the abnormality at the location detected by the sensor, and cannot identify the cause of the failure:
  • the diagnostic systems A and B identify the cause of the failure, but the off-line diagnosis of A has the problem that it takes time for the operator to perform all inspections: The degree of abnormality entered as data Another problem is that depending on the subjectivity, different failure causes are diagnosed by different people.
  • the failure diagnosis is performed based on only the limited inspection items. There is a problem that the accuracy of the diagnosis is deteriorated. Also, when an output from the sensor cannot be obtained due to a malfunction such as a sensor failure, or when the sensor outputs a clearly abnormal value. However, the accuracy of the fault diagnosis is reduced:
  • the present invention has been made in view of such circumstances, and can solve the problem of performing a failure diagnosis only by the online type diagnosis or only the offline type diagnosis, and can obtain an accurate diagnosis result in a short time. It is an object of the present invention to provide a failure diagnosis method and apparatus which can be performed.
  • a failure for diagnosing the failure of the failure diagnosis target based on the relevance data indicating the degree of association between the various inspection items of the failure diagnosis target and the various failure causes of the failure diagnosis target.
  • a knowledge base having relevance data indicating the degree of relevance between various inspection items to be diagnosed and various causes of the failure to be diagnosed is provided.
  • the various inspection items are classified into a sensor inspection item inspected by a detection value of a sensor disposed on the failure diagnosis target and an input data inspection item inspected by input data, and the knowledge base is classified. Create a resource in advance, A threshold value is set for each sensor, and a process of determining whether or not the sensor is abnormal is performed by comparing the set threshold value with a detection value of the sensor.
  • the degree of certainty for each cause of failure is inferred, and based on the inference result, The failure diagnosis target is diagnosed.
  • the inspection items of the sensor can be quickly inspected based on the detection value of the sensor without causing individual differences: Further, the degree of abnormality is obtained from the detection value of the sensor. Inspection items that cannot be checked can be inspected by inputting data as input data inspection items, and accurate inspection results can be obtained with a large number of inspection items that are not limited to sensor inspection items: According to the configuration of the second invention, similarly to the first invention, the degree of abnormality of the sensor inspection item can be quickly obtained based on the detection value of the sensor without causing individual differences. For inspection items for which the degree of abnormality cannot be determined from sensor detection values, the degree of abnormality can be obtained by inputting data as input data inspection items. High accuracy and fault diagnosis results can be obtained:
  • the detected value of each sensor and the result of abnormality determination of each sensor are displayed, and the detected value of the sensor can be changed and corrected as appropriate from the displayed contents, and the degree of abnormality for the calculated sensor inspection item is displayed.
  • the degree of abnormality can be changed and corrected as appropriate from the displayed content: For this reason, even if there is an abnormality such as a failure of the sensor itself or an error when calculating the Can determine abnormalities Since the display contents can be corrected, accurate fault diagnosis results can be obtained without being based on abnormal data.
  • Figure 1 is a fault diagnosis method and device flow Chiya one preparative illustrating a processing procedure of the embodiment of the present invention c
  • FIG. 2 is a flowchart illustrating a processing procedure of another embodiment of the failure diagnosis method and apparatus according to the present invention.
  • FIG. 3 is a diagram showing the contents of a knowledge base for failure diagnosis applied to the embodiment.
  • FIG. 4 is a diagram showing an abnormality degree display screen displayed on the display screen in the process of failure diagnosis.
  • FIG. 5 is a diagram illustrating a membership function for calculating the degree of abnormality.
  • FIG. 6 is a diagram showing a sensor value display screen displayed on the display screen in the process of failure diagnosis.
  • FIG. 7 is a diagram showing a diagnosis result display screen showing the result of the failure diagnosis. Best form for
  • a failure diagnosis of a construction machine is performed: Specifically, a personal computer (hereinafter referred to as a personal computer) performs a failure diagnosis of a construction machine according to the procedure shown in FIG.
  • the knowledge base 1 for fault diagnosis will now be described with reference to FIG. 3.
  • the structure of the knowledge base 1 is basically based on the inspection item i of the construction machine on the line side, that is, “ The battery voltage (low), the exhaust temperature (low), ... and the failure cause j of the row-side construction machine, that is, ⁇ battery failure, injection nozzle failure, electric heater failure, etc.
  • the degree of association Wij indicating the degree of association between these various inspection items i and various failure causes j is preset and stored as data:
  • Various inspection items i include sensor inspection items that are inspected based on the detection values of sensors installed on construction machinery, and input data inspection items that cannot be inspected based on the detection values of sensors (inspection by operators). Items that can be inspected based on the values detected by the sensors may be included in the input data inspection items:
  • Such a knowledge base 1 is set and stored for each fault condition of the construction machine, that is, for “bad engine startability”:
  • a sensor group corresponding to the above-mentioned various sensor check items that is, a battery voltage sensor for detecting a battery voltage, an exhaust temperature sensor for detecting an exhaust temperature, and the like, are naturally provided at a predetermined location on the construction machine before failure diagnosis.
  • These sensors and the personal computer should be wired so that sensor signals can be input to the computer via a predetermined interface-as shown in Fig. 1.
  • the operator operates the keyboard of a personal computer, for example, to check the current failure state of the construction machine to be diagnosed, for example, “Fault (1): Poor engine startability. Is input as data (step 101):
  • each sensor corresponding to the various sensor inspection items shown in the read knowledge base 1 is selected, and the input of the sensor signal is controlled so that the detection value of each of the selected sensors is obtained.
  • the detected values of the battery voltage sensor, exhaust temperature sensor, etc. corresponding to the sensor inspection items “battery voltage”, “exhaust temperature”, etc. shown in FIG. 3 are input and acquired in the personal computer (step 1 0 2)
  • the threshold value of the output is set in advance for each sensor, and whether or not the sensor is abnormal is determined based on whether or not the threshold value is equal to or higher than the threshold value.
  • a first threshold of "0 ° C” A second threshold value of “0 0 0 ° C” is set: Therefore, if the “detected value (exhaust gas temperature) is less than or equal to the first threshold value (0 ° C), (Temperature) is greater than or equal to the second threshold value (100 ° C), the sensor (exhaust temperature sensor) is abnormal: '' is determined in advance.
  • the detection value of the exhaust gas temperature sensor taken in step 102 is applied to the abnormality determination rule to determine whether the exhaust gas temperature sensor is abnormal (step 103);
  • the detection values of each sensor captured in step 102 are displayed on the display screen of the display unit, and the sensor abnormality determination result in step 103 described above corresponding to each sensor detection value is displayed. Is also displayed:
  • the detected values are converted into predetermined engineering units and displayed.
  • the current battery voltage is 12 V
  • the lubricating oil pressure is 20 kg Z cm2.
  • the sensor abnormality judgment result is “normal” or “normal”.
  • the detected value of the sensor is displayed on the sensor value display screen 3.
  • the abnormality degree I i is calculated for each sensor inspection item i:
  • a membership function that is an abnormality evaluation function as shown in Fig. 5 is prepared: Therefore, the final decision in step 104 was made.
  • the exhaust gas temperature is applied to this membership function to calculate the degree of abnormality I i for the inspection item “exhaust gas temperature”. That is, assuming that the exhaust gas temperature is currently 50 °, the arrow in FIG. As shown in (1), the degree of abnormality Ii indicating that the exhaust gas temperature is low due to the membership function is determined to be 0.8 (step 105).
  • the result of the calculation of the degree of abnormality in the above step 105 is displayed on the display screen as the surface 2 of the degree of abnormality display:
  • the abnormality level Ii is displayed for each of the sensor check items i: "low battery voltage”, “low exhaust temperature”, “high lubrication oil pressure”: For example, as described above, regarding “exhaust gas temperature”, the degree of abnormality “0.8” is displayed corresponding to the check item “exhaust gas temperature is low”.
  • the operator can perform a keyboard operation based on experience or the like to change the value.
  • the value can be similarly determined by operating the keyboard. Can be modified.
  • the abnormality level display screen 2 also displays each input data inspection item, and for the abnormality degree corresponding to these input data inspection items, the value determined by the operator based on his / her own experience and the like is operated by keyboard operation.
  • all the abnormalities Ii corresponding to each sensor inspection item are given as those within the appropriate range, and all the abnormalities corresponding to each input data inspection item are obtained.
  • I i is given as being within the proper range, and these abnormalities I i will be displayed on the abnormal degree display section 2 a shown by the broken line on the abnormal degree display screen 2 (step 106).
  • the fault diagnosis is performed: The failure diagnosis is performed by calculating the certainty factor CFj of various failure causes j based on the relevance data Wij of the knowledge base 1 and the abnormality level Ii of each inspection item i obtained in step 106 above. is there:
  • Equation (1) can be used to calculate the confidence factor CFj:
  • Step 107 the calculation result of the above step 107 is displayed on the diagnostic result display screen 4 as shown in FIG. 7: That is, as shown in FIG. , Various failure causes J, that is, “battery failure”, “injection nozzle failure” ... are displayed in units of “25%”, “89%” ...
  • a predetermined cause of failure is selected in the failure cause display section 4a
  • FIG. 2 shows a failure diagnosis processing procedure according to another embodiment.
  • the difference from the flow chart shown in FIG. 1 described above is that the failure input processing corresponding to step 101 in FIG. There is no corresponding action:
  • steps 201 to 207 the same processing as in steps 102 to 108 in FIG. 1 is executed. That is, in this embodiment, the necessary knowledge base 1 is not extracted in accordance with the failure state, and the process proceeds based on the general-purpose knowledge base 11 which does not specify the failure state.
  • step 205 the data from all input data check items will be input as well as the detection values from the sensors corresponding to the check items.
  • various inspection items for performing failure diagnosis are provided. Sensor inspection items and input data items are classified, so sensor inspection items can be inspected quickly without individual differences based on sensor detection values, and are not limited to sensor inspection items Accurate failure diagnosis results can be obtained by many inspection items
  • the detection value of each sensor and the abnormality determination result of each sensor are displayed, and the detection value of the sensor can be appropriately changed and corrected based on the display contents.
  • the abnormalities of the detected sensor inspection items are displayed, and the abnormalities can be appropriately changed and corrected based on the display contents. Therefore, a failure occurs in the sensor itself, or an error occurs when the abnormalities are calculated. Even if there is an abnormality such as an error, it is possible to determine and correct the abnormality based on the displayed content, and to obtain an accurate failure diagnosis result based on no abnormal data.

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  • Engineering & Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

La présente invention concerne un procédé et un dispositif permettant de diagnostiquer les incidents, rapidement et avec précision. Les différents articles de vérification pour diagnostic d'incident se répartissent entre articles de vérification pour capteur et articles de vérification à entrée de données. Concernant les articles de vérification pour capteur, le niveau d'anomalie est calculé à partir de la valeur de détection fournie par le capteur. Concernant les articles de vérification à entrée de données, le niveau d'anomalie provient des données introduites. Pendant son exécution, la procédure de diagnostic d'incident affiche la valeur de détection de chaque capteur ainsi que le résultat d'appréciation d'anomalie correspondant à chaque capteur, les valeurs de détection de chaque capteur pouvant être modifiées ou corrigées à dessein par rapport à ce qui est affiché. La procédure affiche également, pour chaque article de vérification pour capteur, le niveau d'anomalie calculé, ce niveau d'anomalie pouvant être à dessein modifié ou corrigé par rapport à ce qui est affiché. La procédure calcule, pour chacune des différentes causes d'incident, un degré de fiabilité à partir de données représentant le degré d'association entre les différents articles de vérification et les différentes causes d'incidents, la procédure fournissant finalement le niveau d'anomalie.
PCT/JP1995/000171 1994-02-09 1995-02-08 Procede et dispositif de diagnostics d'incidents WO1995022089A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP6015418A JPH07225610A (ja) 1994-02-09 1994-02-09 故障診断方法および装置
JP6/15418 1994-02-09

Publications (1)

Publication Number Publication Date
WO1995022089A1 true WO1995022089A1 (fr) 1995-08-17

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WO (1) WO1995022089A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107195013A (zh) * 2017-05-11 2017-09-22 国网山东省电力公司信息通信公司 一种细粒度控制的运维自动化巡检方法及其***
CN115362421A (zh) * 2020-03-30 2022-11-18 大金工业株式会社 诊断***

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH1144524A (ja) * 1997-07-28 1999-02-16 Matsushita Electric Ind Co Ltd 表面実装型パッケージを用いた電子部品実装装置及びその検査方法
US8719327B2 (en) * 2005-10-25 2014-05-06 Fisher-Rosemount Systems, Inc. Wireless communication of process measurements
JP6585482B2 (ja) * 2015-11-26 2019-10-02 株式会社日立製作所 機器診断装置及びシステム及び方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS59214915A (ja) * 1983-05-20 1984-12-04 Hitachi Ltd 故障波及予測・診断方式
JPS6157868A (ja) * 1984-08-29 1986-03-24 Sumitomo Electric Ind Ltd 自動車故障の診断支援方式
JPH01278866A (ja) * 1988-04-30 1989-11-09 Mazda Motor Corp 車両の故障診断装置
JPH022404A (ja) * 1988-06-13 1990-01-08 Mitsubishi Heavy Ind Ltd プラント故障診断装置
JPH04188307A (ja) * 1990-11-22 1992-07-06 Yamatake Honeywell Co Ltd 故障診断装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS59214915A (ja) * 1983-05-20 1984-12-04 Hitachi Ltd 故障波及予測・診断方式
JPS6157868A (ja) * 1984-08-29 1986-03-24 Sumitomo Electric Ind Ltd 自動車故障の診断支援方式
JPH01278866A (ja) * 1988-04-30 1989-11-09 Mazda Motor Corp 車両の故障診断装置
JPH022404A (ja) * 1988-06-13 1990-01-08 Mitsubishi Heavy Ind Ltd プラント故障診断装置
JPH04188307A (ja) * 1990-11-22 1992-07-06 Yamatake Honeywell Co Ltd 故障診断装置

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107195013A (zh) * 2017-05-11 2017-09-22 国网山东省电力公司信息通信公司 一种细粒度控制的运维自动化巡检方法及其***
CN115362421A (zh) * 2020-03-30 2022-11-18 大金工业株式会社 诊断***
CN115362421B (zh) * 2020-03-30 2024-02-23 大金工业株式会社 诊断***

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Publication number Publication date
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