Fault Diagnosis Method Based on Kurtosis Wave and Information Divergence for Rolling Element Bearings

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 KullbackLeibler (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 transformand 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. Key-Words: Fault Diagnosis, Rolling Element Bearing, Envelope Analysis, Kurtosis Wave, Information Divergence.