Rolling element bearing fault diagnostics using acoustic emission technique and advanced signal processing

Abstract

Acoustic emission (AE) signal generated from defects in rolling element bearings are investigated using simulated defects and experimental measurements in this paper. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings comprises four main stages, which are, statistical analysis, faults diagnostics, defect size calculation, and prognostics. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearing components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) 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 masked by the background noise under different operating conditions.