Fault diagnosis method based on kurtosis wave and information divergence for rolling element bearings

Authors:
Huaqing Wang;Peng Chen
Affiliations:
Diagnosis & Self-recovery Engineering Research Center, Beijing University of Chemical Technology, Chao Yang District, Beijing, China;Graduate School of Bioresources, Mie University, Tsu, Mie, Japan
Venue:
WSEAS TRANSACTIONS on SYSTEMS
Year:
2009

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Abstract

Fault diagnosis depends largely on feature analysis of vibration signals. However, feature extraction for fault diagnosis is difficult because the vibration signals often contain a strong noise component. Noises stronger than the actual fault signal may interfere with diagnosis and ultimately cause misdiagnosis. In order to extract the feature from a fault signal highly contaminated by the noise, and to accurately identify the fault types, a novel diagnosis method is proposed based on the kurtosis wave and information divergence for fault detection in a rolling element bearing. A kurtosis wave (KW) is defined in the time domain using the vibration signal, and a method for obtaining the kurtosis information wave (KIW) is also proposed based on Kullback-Leibler (KL) divergence using the kurtosis wave. A practical example of diagnosis for an outer-race defect in a bearing is provided to verify the effectiveness of the proposed method. This paper also compares the proposed method with two envelope analysis techniques, namely the wavelet transform- and the FFT-based envelope analysis techniques. The analyzed results show that the feature of a bearing defect is extracted clearly, and the bearing fault can be effectively identified using the proposed method.