fault diagnosis capability analysis method based on a hybrid diagnosis Bayesian network

Abstract

The invention discloses a fault diagnosis capability analysis method based on a hybrid diagnosis Bayesian network, and relates to the technical field of fault diagnosis methods. The method comprises the following steps: performing fault probability correlation correction: performing fault probability correlation correction on a component based on two criterion selection of whether reliability datais deficient and whether fault mode fault probability credibility is higher than a function fault probability; Establishing and reasoning a hybrid diagnosis Bayesian network model: selecting a construction mode of the hybrid diagnosis Bayesian network model based on a fault probability correlation correction result, and carrying out hybrid diagnosis Bayesian network model modeling and reasoning;Fault diagnostic capability indicator calculation. According to the method, the accuracy of fault diagnosis analysis modeling and the credibility of a testability index prediction result are improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis methods, in particular to a fault diagnosis ability analysis method based on a hybrid diagnosis Bayesian network. Background technique [0002] At present, due to the lack of consideration of fault propagation and uncertainty factors in the testing process in information flow models and multi-signal flow graph models, many current testability indicators based on fault diagnosis models deviate greatly from actual values, which is not conducive to Quantitatively evaluate the actual fault diagnosis capability level of the product. Contents of the invention [0003] The technical problem to be solved by the present invention is how to provide a method capable of improving the accuracy of fault diagnosis analysis and modeling and the reliability of testability index prediction results. [0004] In order to solve the above-mentioned technical problems, the technical solution adopted by the pr...

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Problems solved by technology

[0002] At present, due to the lack of consideration of fault propagation and uncertainty factors in the testing process in information flow models and multi-signal flow graph models, many current testability indicators based on fault diagnosis models deviate greatly from actual values, which is not conducive to Quantitatively evaluate the actual fault diagnosis ability level of the product

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