The fault detection and severity diagnosis of rolling element bearings using modulation signal bispectrum

The rolling element bearing is a key part in many mechanical equipment. The accurate and timely diagnosis of its faults is critical for predictive maintenance. 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 and inevitable noise. 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 presents a new approach to detection and diagnosis of bearing fault severity based on vibration analysis using modulation signal bispectrum (MSB). 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. The results shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for accurate fault detection and diagnosis for different bearing fault severity.