A novel adaptive resonant band detection method based on cyclostationarity for wheelset-bearing compound fault diagnosis

Bearing condition is crucial to train operation safety. In order to accurately locate the resonant zone caused by bearing failure, a compound fault resonant band detection method is proposed. Firstly, a multi-level frequency band segmentation method based on L2/L1 norm of envelope spectrum is designed. Secondly, based on the results of frequency band segmentations, ICS2, a health index that can accurately characterize the cyclo-stationarity of repetitive transients, is introduced to guide the detection of fault resonant band. By using different multiples of rotation frequency as window length, the proposed method adaptively realizes multi-level band segmentation to extract the subband with the largest ICS2. Moreover, the proposed method has the capability of multiple fault diagnosis. Simulation signals and bench signals verify the effectiveness of the proposed method.

Automated summary

In order to accurately locate the resonant zone caused by bearing failure, a compound fault resonant band detection method is proposed. Firstly, a multi-level frequency band segmentation method based on L2/L1 norm of envelope spectrum is designed. Secondly, based on the results of frequency band segmentations, a health index ICS2 is introduced to guide the detection of fault resonant band.