Circuit fault diagnosis method based on multi-feature information fusion

Abstract

The invention provides a circuit fault diagnosis method based on multi-feature information fusion. The method comprises the steps: extracting fault feature information through the combination of statistical characteristics and integrated empirical mode decomposition, and carrying out dimension reduction on a feature vector through a principal component analysis method to obtain a final fault feature vector; constructing a sample set of a sub-ELM neural network by each fault feature subspace, inputting the sample set into an ELM network model in sequence, training the ELM network model and determining optimal parameters of the model, and performing decision diagnosis by adopting the trained model to obtain initial output; and taking the initialdiagnosis output obtained through the ELM network as different evidence bodies, inputting the evidence bodies into a D-S evidence theory, and obtaining a decision result after diagnosis fusion according to a decision fusion rule. According to the method, the effect of extracting the fault features of the analog circuit is good, and compared with single information, the method has higher diagnosis precision, and can achieve accurate classification of circuit faults, so that the method has good engineering application value.

Description

technical field [0001] The invention relates to a circuit fault diagnosis method based on multi-feature information fusion, and belongs to the technical field of analog circuit fault diagnosis. Background technique [0002] With the rapid advancement of science and technology, the development of electronic technology is rapid. From small products to large systems, it is inseparable from the support of electronic equipment. The complexity of electronic products has also increased, and the connections between components are intricate. Research on analog circuits has shown that nonlinearity, component tolerances, temperature drift, and difficulty in modeling make the diagnostic system of the circuit complex, making it difficult to accurately locate faults, and unexpected circuit failures can lead to serious economic impacts , so the fault diagnosis of analog circuits has become a hot issue in research. Part of the unique performance and output parameters of analog circuits lim...

AI Technical Summary

Problems solved by technology

Research on analog circuits has shown that nonlinearity, component tolerances, temperature drift, and difficulty in modeling make the diagnostic system of the circuit complex, making it difficult to accurately locate faults, and unexpected circuit failures can lead to serious economic impacts , so the fault diagnosis of analog circuits has become a hot issue to be solved urgently in the research

The unique performance and output parameters of analog circuits limit the progress of diagnostic technology. At the same time, traditional theories and methods cannot meet the diagnostic requirements of existing large-scale integrated analog circuits, forcing people to improve and perfect them.

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