Analog Circuits Fault Diagnosis Using ISM Technique and a GA-SVM Classifier Approach

This present work aims to contribute to the solution of the problems encountered in electronic circuits fault diagnosis. One of these troubleshoots faced is the lack of effective features that help to optimize fault classifier and hence improve circuit fault detection and identification. Thus, our feature extraction approach is based on the CUT’s transfer function. This is deduced from the Matlab identification system IS model (ISM), namely the OE model belonging to the ARMA model’s family. These features are the transfer function polynomial coefficients playing a crucial role in the fault free and faulty circuits construction models and feeding the classifier for the fault diagnosis purpose. The faults we are dealing with are of single parametric type. This is done from PSPICE time domain analysis on the CUT output response under theses circuit conditions and followed by extracting the IS model (ISM) orders (p,q) polynomials. The coefficient values of the latter were considered as efficient comparison elements between faulty and healthy circuit responses. As a result, the OE model has achieved 100% fault coverage and its construction reached high accuracy level exceeding 98% for faulty circuits. This accuracy level ambition us to use its coefficients as input features for our Hybrid proposal fault classifier. This is built with GA and SVM algorithms combination targeting both data reduction and fault classification accuracy respectively. The results achieved are conclusive since the classifier accuracy level reached 100% and a 70% of feature data volume reduction was scored.