Size estimation of spall-like fault on rolling element bearing using an integrated approach incorporating total variation regularization and singular spectrum analysis

Abstract

Spalling in rolling element bearings is a common localized defect generated during the operation of a bearing due to rolling fatigue. Size estimation of such localized defects can be helpful in determining the severity of the fault. This can be further used as an input for predicting the remaining useful life of the bearing. The popular approach for estimating the size (in terms of width) of a spall is to trace the entry and exit events of the rolling element while interacting with the fault. The time estimated between entry and exit of a rolling element from a pit-like spall can be converted to a geometric estimation of the fault size from the vibration signature. The present approach demonstrates the use of singular spectrum analysis (SSA) to accomplish this task. The vibration signal generated from the interaction of the rolling element with the localized fault is a hybrid signal consisting of a low frequency stepped response generated while the rolling element enters the fault superimposed onto the high frequency impact generated during the re-entry of the rolling element into the raceway from the spall. The signal information is enhanced via pre-processing with total variation regularization (TVR) filtration. The informative signal, which is extracted from the row temporal signal via SSA, aids in the accurate identification of entry and exit events. The proposed method integrating TVR with SSA for fault size estimation is validated using simulated signals and experimental signals from independent resources. The results show strong agreement with the accuracy level of size estimation.