Multi-source fault detection and diagnosis method and device

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...

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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

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