Journals Proceedings

Abstract

Faults in rolling element bearing are among the main causes of breakdown in rotating machines. Vibration is an effective technique for machine condition monitoring. Vibration signals from a defective bearing with a localized fault contain a series of impulsive responses, which result from the impacts of the defective part(s) with other elements. Most researches carried out have focused on fault location identification. However, limited work has been reported for fault severity estimation, which is critical to make decision for maintenance actions. To improve current diagnostic capability, this paper pays more attention to bearing fault severity diagnosis. It models the vibration sources from bearing defects as an impact process with constant size but three different lengths corresponding to outer race fault, inner race fault and roller fault, respectively. Then an experimental study was followed to evaluate this model. Moreover, the conventional envelope analysis of the measured vibration signals from the tested faulty bearings is optimized by spectral kurtosis (SK) for automatic and reliable fault detection and fault category diagnosis. In the meantime, the diagnostic parameters for fault severity estimation: root mean squared (RMS) values and kurtosis amplitude are developed based on the model results and subsequently evaluated to be agreed vigorously with tested fault cases.