Analog circuit fault diagnosis method based on Bayes-KFCM (Kernelized Fuzzy C-Means) algorithm

Abstract

The invention discloses an analog circuit fault diagnosis method based on a Bayes-KFCM (Kernelized Fuzzy C-Means) algorithm, which comprises the following steps of: carrying out fault diagnosis by adopting a kernelized fuzzy C-means clustering algorithm and firstly judging whether a new fault exits in a test sample, if YES, a diagnostic model of a new fault sample is trained to join a diagnosis system, or else, the fault positioning is carried out on the test sample according to a Bayes fault classification standard. In the invention, the wavelet transform pretreatment is carried out on the fault sample, and the multi-feature fusion is carried out on the wavelet coefficient energy value and the wavelet coefficient fractal dimension value of the sample to extract fault characteristics; and the frequency of an optimal measurable node and / or a test signal is selected through taking a maximum class inter-class distance as a basis. Compared with the prior art, the analog circuit fault diagnosis method realizes that the analog circuit fault diagnosis method, the new fault of an analog circuit can be effectively diagnosed, and the diagnosis accuracy can be improved.

Description

technical field [0001] The invention relates to an analog circuit fault diagnosis method, in particular to an analog circuit fault diagnosis method based on Bayes-KFCM algorithm. Background technique [0002] The test and diagnosis of analog circuits are mainly aimed at the functional test of the circuit. At present, according to the order of simulation in the test process, it can be divided into pre-test simulation and post-test simulation. According to the research literature and patent data in recent years, the intelligent fault diagnosis method is now This method belongs to the pre-test simulation method, and the intelligent diagnosis method can partially solve the problems of ambiguity and uncertainty of analog circuit faults. The most common and widely used intelligent diagnosis method is the neural network method. In recent years, the SVM-based analog circuit fault diagnosis method has achieved great development and has become an important branch of the intelligent di...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the low diagnostic efficiency brought by the tutor learning algorithm in the existing fault diagnosis method and the inability to effectively diagnose new faults, and to provide an analog circuit fault based on the Bayes-KFCM algorithm Diagnosis method, the algorithm is a tutor-less learning algorithm, which has higher diagnostic efficiency and can effectively diagnose new faults

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