Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform

This paper is focused on comparison of effectiveness of artificial intelligence (AI) techniques in fault diagnosis of rolling element bearings. The features for classification are extracted through wavelet packet decomposition using RBIO 5.5 wavelet. The whole classification is done using two features: energy and Kurtosis. The data samples for classification are taken with reference to a healthy bearing, thus, minimizing the errors from the experimental set-up. Four bearing conditions such as bearing with outer race defect, inner race defect, ball defect and combined defect on outer race, inner race and ball have been used in this paper. Localized defects of micron level are induced through laser machining. The effectiveness of three AI techniques viz. ANN, SVM and multinomial logistic regression are compared. The results show that the Logistic Regression technique is the more effective than other two techniques as ANN and SVM.