Signal processing techniques for rolling element bearing spall size estimation. (15th February 2019) - Chen, Aoyu; Kurfess, Thomas R.
Highlights: The empirical model of the entry event improves the precision to identify the entry point from the bearing vibration signal to improve estimation result. The VMD is used to remove high frequency noises from the entry signal to help identify the entry point. Signal processing techniques developed in this paper are easier to be implemented in an automatic diagnostic system. Abstract: This paper proposes signal processing techniques to extract entry and exit points from the bearing vibration signal for spall size estimation. The entry point is the start point of a low frequency response when a rolling element enters a localized defect on the raceway, which is contaminated with background noises and difficult to identify. An empirical model based signal processing method is proposed to effectively identify the entry point. The Variational Mode Decomposition (VMD) is applied to the bearing entry signal for more accurate estimation. Differentiation technique is used to identify the high frequency exit point with more reliable threshold values for automatic diagnostics. Then, based on defect size estimation models for both inner and outer race defects, the spall size can be estimated. The proposed methodology is validated on a machine tool spindle's bearing system. Experimental results show that the proposed signal processing techniques provide less biased results with respect to spindle speed and more precise estimation.