Intelligent fault diagnosis method based on rough Bayesian network classifier

Abstract

The invention provides an intelligent fault diagnosis method based on a rough Bayesian network classifier, which comprises the following steps: using standard fault feature data as a fault diagnosis condition attribute set, using a standard fault mode as a fault diagnosis decision attribute set, and adopting a rough set principle to construct an original fault diagnosis information table T1; adopting the minimum entropy method to carry out discrete processing on various continuous fault diagnosis condition attribute values in the T1, so as to form a discretization fault diagnosis information table T2; using a rough set discernable matrix and a nuclear theory to carry out attribute reduction and optimal feature selection on the T2, so as to form a reduction fault diagnosis information table T3; and using the T3 to establish the Bayesian network classifier, so as to realize efficient and quick intelligent fault diagnosis. The intelligent fault diagnosis method avoids the 'curse of dimensionality' problem existed in a Bayesian network diagnostic method, overcomes weaknesses of rigid reasoning and critical misjudgment in a rough set diagnostic method, and greatly improves the efficiency and accuracy of fault diagnosis.

Description

technical field [0001] The invention relates to the field of electromechanical technology, in particular to an intelligent fault diagnosis method. Background technique [0002] In the fault diagnosis process, due to the unclear mechanism of the fault and the non-unique form of the fault, there is often a certain degree of blindness in the extraction of various parameters describing the characteristics of the fault, resulting in unclear fault states. of. Rough set theory can start from the original data describing the fault state, and under the premise of ensuring that the information is not lost, effectively reduce the decision-making system, remove redundant information, and at the same time extract and reduce rules, so as to effectively solve the above problems. However, if rough sets are used alone for fault diagnosis, there are often the following two problems: ① the processing of rules is mostly based on the logic theory of the knowledge base, which is not efficient; ...

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Problems solved by technology

[0005] In order to overcome the disadvantages of low efficiency and misjudgment in the prior art, the present invention provides an intelligent fault diagnosis method based on a rough Bayesian network classifier, which can not only extract key condition attributes from diagnostic data, but also reduce Bayesian The scale of the Bayesian n

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