Fault diagnosis of a rolling element bearing based on AR model and spectral kurtosis

The auto-regressive(AR) model was an important tool for stationary signal analysis.The optimum AR model order was determined with the Maximum Kurtosis Criterion,and then this AR model was used to pre-process fault signals obtained from a rolling element bearing.As a result,it eliminated the linearly predictable stationary part and achieved the residual component only containing noise and the non-stationary part of the signal.Consequently,the difficulty of the following signal analysis was eased.Spectral kurtosis(SK) was sensitive to non-stationary signals,it could extract the non-stationary part from a noisy signal.Here,the AR model and SK were combined and used to more effectively detect faults of rolling element bearings.The effectiveness of the proposed method was verified by the test results.