Rolling element bearing condition monitoring using acoustic emission technique

Abstract

Acoustic emission (AE) signals generated from defects in rolling element bearings are investigated analytically and experimentally in this paper. Roller element bearings are crucial parts of many machines and there has been an increasing demand for effective and reliable health monitoring of these data elements and for finding optimum procedures for the signal processing of the measured data to detect and diagnose the size and location of incipient defects in rolling element bearings. This paper describes a novel signal processing algorithm designed to diagnose localized defects on rolling element bearings components which has recently been tested for different operating speeds and loadings. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transforms and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals under different operating conditions. Also, the effects of defect size, operating speed, and loading conditions on AE burst duration have also been investigated to estimate the fault size on the outer race