Diagnostics of Filtered Analog Circuits with Tolerance Based on LS-SVM Using Frequency Features

Most researchers have used the optimal wavelet coefficients or wavelet energy indicators from the time-domain response of analog circuits to train support vector machines (SVMs) to diagnose faults. In this study, we have proposed two kinds of feature vectors from frequency response data of a filter system to train least squares SVM (LS-SVM) to diagnose faults. The first is defined as the conventional frequency feature vector, which includes the center frequency and the maximum frequency response. The second is a new wavelet feature vector that is composed of the mean and standard deviation of wavelet coefficients. Different feature vectors’ combination and normalization are also discussed in the paper. The results from the simulation data and the real data for two filters showed the following: (1) The proposed method has better diagnostic accuracy than the traditional methods that were based only on the optimal wavelet coefficients or wavelet energy indicators. (2) The diagnostic accuracies using the combined feature vectors were better than those using only the conventional frequency feature vectors or wavelet feature vectors. (3) The best accuracy from using the conventional frequency feature vectors was better than that from using wavelet feature vectors. The proposed method can be extended to diagnostics of other analog circuits that are determined by their frequency characteristics.