Analog Circuits Fault Diagnosis Using
ISM Technique and a GA-SVM Classifier Approach
Sabah Kouachi, Nacerdine Bourouba, Kamel Mebarkia, and Imad Laidani
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.