Bing Long1,3, Shulin Tian1, Qiang Miao2,3, and Michael Pecht3
1School of Automation Engineering, University of Electronic Science and Technology of China (UESTC),
Chengdu 611731, China
2School of Mechanical, Electronic and Industrial Engineering, UESTC, Chengdu 611731, China
3Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742,USA
Feature selection techniques have become an apparent need for diagnostic methods such as a least squares support vector machine (LS-SVM). Most researchers use wavelet transform coefficients of the time-domain transient response data obtained from filtered analog circuits as features to train a LS-SVM classifier to diagnose faults. But wavelet coefficient features have certain disadvantages such as no physical meanings. Thus, in this paper, two new feature vectors with clearly defined meanings based on a time-domain response curve and a frequency response curve of a filter are proposed, respectively. In addition, a statistical property feature vector which represents global properties of the time domain response curve or the frequency response curve is proposed. The results from the simulation data and real data for a bi-quad filter showed the following:
(1) these proposed conventional time-domain and frequency features, which are already familiar to designers of filtered analog circuits, have good diagnostic accuracy—all above 91% for the example circuit;
(2) the best accuracies using the proposed statistical property feature vector are 100% for time-domain simulation data, and for both real experiment data ; (3) the diagnostic accuracy using the proposed combined feature vector is more accurate than conventional feature vectors;
(4) an LS-SVM can be used to diagnose faults in a real analog circuit that only has a few fault samples.
Keywords: diagnostics; feature selection; feature vector; filtered analog circuits; least squares support vector machine (LSSVM); time-domain features; frequency features
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