Rolling bearing fault diagnosis based on sensor information fusion and generalized cyclic cross correntropy spectrum density

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (2) : 200-207.

LI Hui1,HAO Rujiang2

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Abstract

In the field of fault diagnosis and identification, the reliability of fault feature extraction and recognition based on single sensor vibration signal is lower. The traditional spectral correlation density method has poor time-frequency aggregation ability. In order to solve these problems, a bearing fault diagnosis method based on multiple sensors information fusion and generalized cyclic cross correntropy spectrum density is proposed. Firstly, the definition and calculation formula of generalized cross correntropy, generalized cyclic cross correntropy and generalized cyclic cross correntropy spectral density are deduced in detail. Then the de-noising performance of generalized cyclic cross correntropy is analyzed and verified by means of a simulative bearing outer race defect signal, which shows that generalized cyclic cross correntropy can effectively deal with Gaussian and non Gaussian noise. Finally, the generalized cyclic cross correntropy is applied to the fault detection and identification of the motor bearing inner and outer race defect. The experimental results show that the generalized cyclic cross correntropy can effectively extract the fault characteristics of the rolling bearing, and the generalized cyclic cross correntropy can accurately describe the spectral characteristics of the bearing defect. The performance of generalized cyclic cross correntropy spectral density is better than that of the traditional spectral correlation density method.

Key words

Generalized cross correntropy / Generalized cyclic cross correntropy function / Generalized cyclic cross correntropy spectral density function / Cyclostationary signals / Bearing / Fault diagnosis

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LI Hui1,HAO Rujiang2. Rolling bearing fault diagnosis based on sensor information fusion and generalized cyclic cross correntropy spectrum density[J]. Journal of Vibration and Shock, 2022, 41(2): 200-207

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