Fault diagnosis method of rolling bearing based on IMCKD and MCCNN

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (7) : 241-249.

LIU Haobo1, HAO Hongtao1,2, DING Wenjie1

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Abstract

Due to the influence of external environment noise and vibration noise coupling, the early faults vibration signals of the rolling bearing are very weak, which is a challenge to realize intelligent fault diagnosis. In order to solve the above problems, an intelligent fault diagnosis method based on the improved maximum correlation kurtosis deconvolution（IMCKD） and multi-channel convolution neural network（MCCNN） is proposed. Firstly, the firefly algorithm is used to search the two influence parameters of the maximum correlation kurtosis deconvolution in parallel, and the original vibration signals is adaptively filtered to get the diagnostic data source. Then it is input to MCCNN for feature learning so that the network parameters are continually updated. Finally, the features are applied to the classifier recognition to realize the intelligent fault diagnosis of rolling bearing. In order to verify the feasibility and effectiveness of the intelligent method, the data collected from the rolling bearing fault simulation test rig are used to verify the algorithm. The experimental results show that the method can classify the fault types of rolling bearing accurately and effectively, and it has more than 90% fault recognition rate even under strong background noise, and it has good stability and generalization ability.

Key words

Rolling bearing / Maximum correlation kurtosis deconvolution / Convolution neural network / Firefly optimization / Feature learning

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LIU Haobo1, HAO Hongtao1,2, DING Wenjie1. Fault diagnosis method of rolling bearing based on IMCKD and MCCNN[J]. Journal of Vibration and Shock, 2022, 41(7): 241-249

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