Wavelet transform for bearing condition monitoring and fault diagnosis: A review

Condition monitoring (CM) and fault diagnosis of rolling element bearings is essential for smooth and safe running of machines. Signal analysis is an important component of condition monitoring and fault diagnosis. Wavelet transform (WT) has been widely used for signal analysis, particularly in condition monitoring, for the past several years. WT and its applications and new developments in this area are increasing at a rapid rate. Hence it is essential to review the literature in order to understand the current trends in this new and emerging area of signal processing. In this regard, this paper will review application of WT to CM and fault diagnosis of rolling element bearing (REB). The review will cover some broad areas of research like: time-frequency analysis of signals, fault feature extraction, singularity detection, denoising and various pattern recognition techniques like artificial neural network (ANN), support vector machine (SVM) and Fuzzy logic. This also covers some new and recent developments in the application of WT. A summary of some of the major developments happening in this field is presented at the end.