Ann Based Fault Diagnosis of Rolling Element Bearing Using Time-frequency Domain Feature

This paper presents a methodology for an automation of fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components. The system uses the wavelet packet decomposition using ‘rbio5.5’ real mother wavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The decomposition level is determined by the sampling frequency and characteristic defect frequency. Maximum energy to minimum Shannon entropy ratio criteria is used for selection of best node of wavelet packet tree. The two features kurtosis and energy are extracted from the wavelet packet coefficient for selected node of WPT. The total 10 data sets at five different speeds corresponding to each bearing condition are recorded for fault classification. Thus, extracted features are used to train and test neural network with multi layer perceptron to classify the rolling element bearing condition as HB, ORD, IRD, BD and CD. The proposed artificial neural network with multi layer perceptron classifier has overall fault classification rate of 97 %.