Phasor Analysis Based Fault Modeling and Fault Diagnosis Methods for Linear Analog Circuits

Abstract

Soft fault diagnosis and tolerance are two challenging problems in linear analog circuit fault diagnosis. To solve these problems, a phasor analysis based fault modeling method and its theoretical proof are presented at first. Second, to form fault feature data base, the differential voltage phasor ratio (DVPR) is decomposed into real and imaginary parts. Optimal feature selection method and testability analysis method are used to determine the optimal fault feature data base. Statistical experiments prove that the proposed fault modeling method can improve the fault diagnosis robustness. Then, Multi-class support vector machine (SVM) classifiers are used for fault diagnosis. The effectiveness of the proposed approaches is verified by both simulated and experimental results.

An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

“…These points and their tolerance areas will greatly affect the classification accuracy. Therefore, the fuzzy groups must be handled effectively before diagnosing circuit faults [17,18]. Considering the indistinguishability between 2D voltage features in the complex domain, this paper puts forward a 3D voltage feature construction method.…”