A statistical feature investigation of the spalling propagation assessment for a ball bearing

Abstract

Spalling is a main fatigue failure type of ball bearings. Vibration features of the bearing will be changed during the spalling propagation, which can be utilized to identify the spalling damage level. In this study, a new spalling propagation assessment algorithm dependent on the spectrum amplitude ratio and statistical features is established to identify the spalling damage location and level. The damage level is determined by the test fault samples in the listed test works. The spectrum amplitude ratio based on the bearing fault frequencies and spectrum amplitudes is applied to identify the damage location. 25 statistical features of the time-domain vibration signal are calculated. Pearson correlation coefficient (PCC) is used to determine the effective ones in the 25 statistical features presented by the previous works. The effective statistical features are applied to estimate the damage level. The test data given by the previous work in the list reference is utilized to verify the developed spalling propagation assessment algorithm. The results indicate that the established method can give a new approach to identify the spalling damage location and level of a ball bearing.