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Multi-source fault detection and diagnosis method and device

A technology of fault detection and diagnosis method, applied in the direction of comprehensive factory control, instrumentation, comprehensive factory control, etc., can solve the problems of not considering the intricate relationship of variables, single fault type, etc., to achieve the effect of rapid positioning and improved accuracy

Active Publication Date: 2019-07-09
QILU UNIV OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0004] Aiming at the problem that the existing traditional technologies perform fault detection and diagnosis when the fault type is single and the influencing factors are simple, without considering the intricate correlation between variables in complex industrial processes, this disclosure provides a multi-level based Multi-source fault detection and diagnosis method and device based on knowledge map and Bayesian theoretical reasoning, which considers the intricate correlation between variables in complex industrial processes, and improves the accuracy of fault detection and diagnosis technologies in complex industrial processes

Method used

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  • Multi-source fault detection and diagnosis method and device

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Embodiment 1

[0057] This embodiment provides a multi-source fault detection and diagnosis method based on multi-level knowledge graphs and Bayesian theoretical reasoning. Firstly, the historical data of complex industrial systems under normal conditions are used to analyze from different levels, and multi-level knowledge under normal conditions is constructed. Atlas, dig out the deep-level correlation path of the multi-level knowledge map in the normal state; obtain the data to be detected in the complex industrial system to be detected, calculate the discrimination coefficient R, and judge whether the system is in a fault state. If the system is in a fault state, enter Fault diagnosis stage; in the fault diagnosis stage, the system fault variables are determined according to the deep-level correlation path, and the multi-level knowledge graph fault model under the state to be detected is constructed according to the determined fault variables; the multi-level knowledge graph fault model is ...

Embodiment 2

[0127] This embodiment provides a multi-source fault detection and diagnosis device, which includes:

[0128] The deep-level correlation path acquisition module is used to obtain the historical data under the normal state of the system, construct the multi-level knowledge map under the normal state, and mine the deep-level correlation path of the multi-level knowledge map under the normal state; obtain the system under the current state to be detected For the data to be detected, construct a multi-level knowledge graph under the state of detection, and mine the deep-level correlation path of the multi-level knowledge graph under the state of detection;

[0129] A fault detection module is used to judge whether the data to be detected is in a fault state;

[0130] The fault diagnosis module is used to determine the fault variables if the system is in a fault state, and construct a multi-level knowledge map fault model under the state to be detected; use the multi-level knowledg...

Embodiment 3

[0157] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps in the above-mentioned multi-source fault detection and diagnosis method are realized.

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Abstract

The present invention discloses a multi-source fault detection and diagnosis method and device. The method includes the following steps: obtaining historical data in a normal state of a system, constructing a multi-level knowledge map in a normal state, and mining a deep-level association path of the multi-level knowledge map in a normal state; obtaining data to be detected in a current to-be-detected state of the system, constructing a multi-level knowledge map under the to-be-detected state, and mining the deep-level association path of the multi-level knowledge map in the to-be-detected state; determining whether the data to be detected is in a fault state; if the system is in a fault state, a fault variable is determined, constructing a multi-level knowledge map fault model under the to-be-detected state; and performing multi-source fault diagnosis by using the multi-level knowledge map fault model and combing with a Bayesian theory.

Description

technical field [0001] The present disclosure relates to the field of complex industrial process detection, in particular to a multi-source fault detection and diagnosis method and device based on multi-level knowledge graph and Bayesian theoretical reasoning. Background technique [0002] With the continuous expansion of scale and complexity of modern industrial processes, people have to put the reliability and safety of process production in an important position. As an important part of process monitoring, fault detection and diagnosis technology has achieved considerable development. Various intelligent algorithms and pattern recognition methods have been widely used in process monitoring in various fields. [0003] In the actual process monitoring, due to the expansion of the scale and complexity of the industrial process, there are complex correlations among the process variables in the production process. At the same time, as the technological process becomes more an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCG05B19/41885G05B2219/32339Y02P90/02
Inventor 孙涛王琦王新刚郭爱章
Owner QILU UNIV OF TECH
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