Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Recently, the mechanical fault detection of an induction motor (IM) from vibration signals using pattern recognition has proven to be an effective method. This paper has studied for the first time statistical time domain features mean absolute value (MAV), waveform length (WL), zero crossing(ZC), slope sign changes (SSC), simple sign integral(SSI) and Willison amplitude (WAMP) for identification of the mechanical faults using linear discriminant analysis (LDA) and naive Bayes (NB) classifiers. In this study, the effectiveness of the features is investigated using parameters like accuracy, sensitivity and specificity individually and in groups for a total of 63 combinations. Each feature set combination is investigated for 15datasets defined under 5 groups in different combinations of faulty and normal working conditions. The results indicate that the feature set of SSI, WL,SSC and ZC features outperform the conventional features in the identification of faults and is found to be computationally effective. Further, NB classifier is found to be better than LDA in identification of mechanical faults.