An extension of unscented Kalman filter to dynamic Bayesian wavelet transform in fault diagnosis of rolling element bearings

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (11) : 53-62.

ZHAO Jing1,2， LIAO Yingying2,3， YANG Shaopu1,2， LIU Yongqiang1,2， GU Xiaohui1,2

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Abstract

In engineering practice, the types of rolling bearing failure are typical and the fault signals are impulsive, and the frequency component of the vibration signals are extremely complex due to the influence of the external environment. A dynamic bayesian wavelet transform method based on negentropy and unscented kalman filter is proposed. The method applies SE(Squared Envelope) Infogram method to Unscented Kalman Filter (UKF) method. This method uses SE Infogram to determine the initial value of filter parameters, that is initial values of center frequency and bandwidth. Then, the center frequency and bandwidth are optimized with UKF, the vibration signal is filtered by optimal center frequency and bandwidth, and the envelope demodulation of the filtered signal is analyzed, so the weak fault characteristics of bearing can be extracted. This method replaces the kurtosis index used in previous studies with negentropy index. which can effectively eliminate or weaken the influence of peak value interference. Finally, the simulation signal and wheelset bearing test signal are validated. The results indicate that this method can effectively extract the fault of bearing outer and inner and roller under strong background noise, the effectiveness of this method in the diagnosis of weak bearing faults is verified.

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fault diagnosis / negentropy / unscented kalman filter / dynamic bayesian wavelet transform

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ZHAO Jing1,2， LIAO Yingying2,3， YANG Shaopu1,2， LIU Yongqiang1,2， GU Xiaohui1,2. An extension of unscented Kalman filter to dynamic Bayesian wavelet transform in fault diagnosis of rolling element bearings[J]. Journal of Vibration and Shock, 2020, 39(11): 53-62

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