Method for Fault Diagnosis of Analog Circuits Based on Feature Selection

Traditionally, multi-fault diagnosis of analog circuits based on least squares support vector machine (LSSVM) usually uses a single feature vector combination to train all binary LSSVM classifiers. However, in fact, each binary LSSVM classifier has different classification accuracy for different feature vector combinations. Therefore, the Mahalanobis distance (MD) based on particle swarm optimization (PSO) is proposed to select a near-optimal feature vector combination for each binary classifier. Then, the near-optimal feature vector combinations are used to train and test LSSVM for diagnostics of the incipient faults in analog circuits. The experimental results show that the accuracy using the near-optimal feature vector combinations is higher than the accuracy using a single vector combination.