A Quantitative Fault Diagnosis Method for Rolling Element Bearings Based on Dynamic Model and Fast Spectral Kurtosis

Quantitative analysis of rolling bearing fault is a crucial issue in the condition monitoring of rotating machines. Therefore, a quantitative analysis method of bearing fault is presented in this paper based on the unit of dynamic mechanism and feature extraction algorithm of vibration signal. Based on this method, vibration signals for different sizes of rolling bearing fault in the outer race were simulated using a dynamic bearing model with five freedom degrees. Then, the quantitative size feature was extracted from the vibration signals with an algorithm of fast spectral kurtosis (FSK) and quantitative diagnosis of rolling bearing fault was obtained. The effectiveness of the proposed method was verified by practical defective bearing signals of different defective sizes.