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The frequency of rolling element failures in induction motor is high and may lead to losses due to sudden downtime of machine. Researchers are fervent to identify an effective fault diagnosing scheme with less computational burden using optimum number of good discriminating features. We attempted time domain features, namely, waveform length (WL), slope sign changes (SSC), simple sign integral and Wilson amplitude for the first time in addition to established mean absolute value and zero crossing (ZC) for identification of mechanical faults of induction motor. Ten data sets are derived from publicly available vibration database of Case Western Reserve University to identify the capability of features in identification of faults under various conditions. The results are compared with six conventional features for tenfold cross validation using linear discriminant analysis, naive Bayes, and support vector machine classifiers. The results have shown that WL, WAMP, ZC, and SSC outperform other features. Furthermore, area under receiver operator characteristics curve analyses showed an average of 0.9987 with the proposed statistical features and 0.97618 with six conventional features. We also attempted to study the effect of data length and percentage of overlap in classification and found accuracy improves with increase in length but not significant beyond the window length of 3000 with 50% overlap. The proposed statistical features are validated using the brute force method and Laplaician method of feature selection and shown an average accuracy rate of 0.9936 and 0.9894, respectively.
Nayana et al. (Wed,) studied this question.