Analog circuit fault diagnosis method based on cascade connection integrated classifier

Abstract

The invention discloses an analog circuit fault diagnosis method and an implementation method of the analog circuit fault diagnosis method. The content includes the first part of analog circuit fault feature information extraction, the second part of fault classifier construction, and the third part of implementation of algorithm software. The analog circuit fault diagnosis method includes the following steps of constructing a fault feature information base, selecting an optimal mother wavelet through an information entropy maximizing principle, conducting wavelet decomposition on response nodes of a measured circuit, extracting the optimal feature of the measured circuit, conducting dimensionality reduction on the fault features through principal component analysis, conducting fault classification and intelligent diagnosis, constructing a fault diagnosis device according to the obtained fault feature information and through a multi-classifier cascade connection model and the classifier integration technology so as to recognize existing faults and causes of the faults, and conducting specific implementation on the algorithm through a C#.NET platform and through combination with the Weka software. The diagnosis method and the implementation method have the advantages of being high in fault diagnosis performance, wider in diagnosis range, higher in algorithm robustness and higher in interpretability.

Description

technical field [0001] The invention relates to a fault diagnosis method of an analog circuit and its realization. Background technique [0002] Fault diagnosis of analog circuits began in the 1960s, and its theoretical research started from the solvability of network element parameters. However, due to its unique difficulties such as diversity of fault states, tolerance of element parameters, insufficient information and Due to the complexity of the structural model, etc., the development of research on fault diagnosis of analog circuits is relatively slow, and its testing and fault diagnosis has always been a problem that plagues the circuit testing industry. After the 1990s, with the development of artificial intelligence technology, fuzzy theory, wavelet technology and some machine learning methods have been applied in this field and achieved good results, but they all have one-sidedness and have no effect on solving actual analog circuit faults. There is still a certai...

AI Technical Summary

Problems solved by technology

a) The fault diagnosis device uses a multi-classifier cascade model, firstly to solve the problem that it is difficult to distinguish between normal samples and early fault samples, that is, firstly, the characteristic samples of normal circuits and the samples of all fault circuits form two disjoint subsets, Using homomorphic integration technology to construct a support vector machine classifier, forming a level G 0 , used to distinguish between normal and faulty states; secondly, for faulty samples, the base classifiers of different algorithms are trained using heterogeneous integration technology, and then the classifiers are merged using the weighted voting algorithm to form a hierarchy G 1 , used to distinguish different fault states, this two-level structure forms a multi-category cascade reasoning idea

Images