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<p>Due to the uncertainty existing in the actual industrial environment, the rolling bearing compound fault features present coupling and complexity, which brings challenges to the compound fault feature extraction. To address this problem, this paper proposes a rolling bearing compound fault diagnosis method AMOMCKD-CNN based on adaptive multi-objective maximum correlation kurtosis deconvolution (AMOMCKD) and convolutional neural network (CNN) with parameter optimization. Firstly, the key parameters of MCKD are optimized using the adaptive Non-dominated Sorting Genetic Algorithm (NSGA-II) with a new multi-objective evaluation index Hyperarea (HA). Secondly, the optimized MCKD is used as a filter to extract the periodic pulse characteristics of the original vibration acceleration signal. Finally, the kernel size of the CNN is optimized based on the length of the filtered periodic pulse signal, which enables the CNN to achieve deeper feature extraction and classification. Experimental results from two different datasets highlight that AMOMCKD-CNN outperforms other classical diagnostic methods under the same conditions, and it is more conducive to the detection of compound faults.</p>

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Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月24日

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  • Runfang Hao,
  • Yunpeng Bai,
  • Kun Yang,
  • Yongqiang Cheng,
  • Shengjun Chang

摘要

Due to the uncertainty existing in the actual industrial environment, the rolling bearing compound fault features present coupling and complexity, which brings challenges to the compound fault feature extraction. To address this problem, this paper proposes a rolling bearing compound fault diagnosis method AMOMCKD-CNN based on adaptive multi-objective maximum correlation kurtosis deconvolution (AMOMCKD) and convolutional neural network (CNN) with parameter optimization. Firstly, the key parameters of MCKD are optimized using the adaptive Non-dominated Sorting Genetic Algorithm (NSGA-II) with a new multi-objective evaluation index Hyperarea (HA). Secondly, the optimized MCKD is used as a filter to extract the periodic pulse characteristics of the original vibration acceleration signal. Finally, the kernel size of the CNN is optimized based on the length of the filtered periodic pulse signal, which enables the CNN to achieve deeper feature extraction and classification. Experimental results from two different datasets highlight that AMOMCKD-CNN outperforms other classical diagnostic methods under the same conditions, and it is more conducive to the detection of compound faults.