Fault diagnosis of rolling element bearing based on ar model and spectral kurtosis
Shi Lin-suo,Shen Jin-wei, Zhang Ya-zhou, Niu Wu-ze
Author information+
Department No.5, the Second Artillery Engineering University, Xi’an 710025, China
{{custom_zuoZheDiZhi}}
{{custom_authorNodes}}
{{custom_bio.content}}
{{custom_bio.content}}
{{custom_authorNodes}}
Show less
History+
Received
Revised
Published
2010-08-01
2010-10-25
2011-12-25
Issue Date
2011-12-25
Abstract
the Auto-Regressive (AR) model was an important tool for stationary signal analysis. The optimum AR model order was determined by the Maximum Kurtosis Criterion, and then this AR model was used to pre-processed the fault signal which obtained from rolling element bearing. As a result, it eliminated the liner predictable stationary part and achieved the residual error, which only contained the noise and the non-stationary part of the signal. Consequently, the difficulty of following signal analysis was eased. Spectral Kurtosis(SK) was sensitive to non-stationary signals, it could extract the non-stationary part from the noisy background, and so, aiming at a more efficient result of the rolling element bearing diagnosis, this essay combined the AR model and Spectral Kurtosis to detect the fault. And the expected result was achieved.
Shi Lin-suo;Shen Jin-wei;Zhang Ya-zhou;Niu Wu-ze.
Fault diagnosis of rolling element bearing based on ar model and spectral kurtosis[J]. Journal of Vibration and Shock, 2011, 30(12): 257-260
PDF(1378 KB)
