Adaptive time-domain sliding average method for spall size estimation of roller bearing with skidding

Abstract

The size of the bearing spall fault can be estimated by the time interval between the roller's entry and exit points in the bearing vibration signal. However, the entry vibration response is typically low amplitude and masked in environmental noise. The time-domain average (TDA) method is effective for periodic impulse enhancement. However, bearing skidding is unavoidable during its running, and the cage's speed fluctuations caused by skidding will lead to non-periodicity of the impulses. The performance of TDA seriously deteriorated with such skidding because of the time-varying period. This paper presents the adaptive time-domain sliding average (ATDSA) method to address the bearing skidding problem. The similarity feature between each impulse segment is utilized, and a time delay matrix is constructed to estimate the skidding rate and mitigate the skidding effect. The frequency spectrum of the reconstructed signal shows clear sidebands with BCF interval. And the obtained averaged waveform has high resolution in both the low-frequency entry and high-frequency exit events. Simulation and experiment results show that it can effectively suppress the skidding effect and estimate the spall size correctly under different speeds.