Fault diagnosis of rolling bearing under strong background noise based on SSA-VMD-MCKD

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (3) : 217-226.

REN Liang, ZHEN Longxin, ZHAO Yun, DONG Qiancheng, ZHANG Yunpeng

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Abstract

In order to effectively extract the weak fault characteristics of rolling bearing and accurately diagnose the fault in the environment of strong background noise, a rolling bearing fault diagnosis method combining singular spectrum analysis (SSA), variational mode decomposition (VMD) and maximum correlated kurtosis deconvolution (MCKD) was proposed. Firstly, the fault signal was decomposed by SSA algorithm, and the decomposed signal was filtered and reconstructed according to the time-domain cross-correlation criterion; Secondly, the whale optimization algorithm (WOA) was used to optimize the parameters alpha, K of VMD and L and M of MCKD respectively. The reconstructed signal was decomposed by the parameter optimized VMD, and the fault characteristic signal was extracted from the decomposed intrinsic mode function (IMF) according to the kurtosis index; Thirdly, the parameter optimized MCKD algorithm was used to enhance the impact characteristics in the fault characteristic signal; Finally, fault diagnosis was carried out through spectrum envelope. Simulation and experiments show that the proposed method can effectively extract and diagnose bearing faults under the interference of strong background noise.

Key words

singular spectrum analysis (SSA) / variational modal decomposition (VMD) / maximum correlation kurtosis deconvolution (MCKD) / whale bionic optimization algorithm (WOA) / bearing fault diagnosis

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REN Liang, ZHEN Longxin, ZHAO Yun, DONG Qiancheng, ZHANG Yunpeng. Fault diagnosis of rolling bearing under strong background noise based on SSA-VMD-MCKD[J]. Journal of Vibration and Shock, 2023, 42(3): 217-226

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