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<p>A rolling bearing fault diagnosis method combining mutant dung beetle optimizer (MDBO) and successive variational mode decomposition (SVMD) is proposed to address the challenge of noise, weak, and nonlinear fault signals. Firstly, an MDBO algorithm is designed to achieve optimization tasks faster and more accurately through improved global search and local development strategies. Secondly, the MDBO algorithm is used to select the optimal parameters for SVMD, which decomposes the bearing vibration signal without relying on manually set parameters. Then, a multi-dimensional comprehensive index Q is developed to select and reconstruct SVMD components, focusing on fault information. Finally, envelope demodulation analysis is applied for fault diagnosis. Simulation and experimental results demonstrate that the proposed method exhibits superior noise reduction and feature extraction, enabling more effective rolling bearing fault diagnosis.</p>

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Rolling bearing fault diagnosis based on MDBO-SVMD

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Chunli Lei,
  • Jiyang Zhou,
  • Manwen Li,
  • Dongfeng Hao,
  • Xinjie Li,
  • Cheng Li,
  • Ruicheng Feng

摘要

A rolling bearing fault diagnosis method combining mutant dung beetle optimizer (MDBO) and successive variational mode decomposition (SVMD) is proposed to address the challenge of noise, weak, and nonlinear fault signals. Firstly, an MDBO algorithm is designed to achieve optimization tasks faster and more accurately through improved global search and local development strategies. Secondly, the MDBO algorithm is used to select the optimal parameters for SVMD, which decomposes the bearing vibration signal without relying on manually set parameters. Then, a multi-dimensional comprehensive index Q is developed to select and reconstruct SVMD components, focusing on fault information. Finally, envelope demodulation analysis is applied for fault diagnosis. Simulation and experimental results demonstrate that the proposed method exhibits superior noise reduction and feature extraction, enabling more effective rolling bearing fault diagnosis.