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<p>Aiming at the problems in the compound fault diagnosis of locomotive axle box bearings, where it is difficult to accurately separate and extract fault components, and the coupling effect between noise and compound fault components leads to misdiagnosis or missed diagnosis, this paper proposes an adaptive diagnosis method for compound faults in axle box bearings. First, the APSO algorithm is used to adaptively optimize the core parameters of VMD, effectively overcoming the limitation that traditional VMD parameters are set based on experience. Second, signal reconstruction is performed based on the correlation criterion between the intrinsic mode function (IMF) and the original signal, and the main fault of the bearing is identified through spectrum analysis. Then, after filtering out strong interference components, secondary fault features are extracted. Finally, through the synergistic effect of APSO-VMD and the adaptive notch filter, effective separation and accurate diagnosis of compound faults are achieved. MATLAB simulation and experimental data verification show that this method has good robustness and accuracy in bearing compound fault diagnosis, and the average relative error of its extracted characteristic frequencies is less than 4%. Meanwhile, this method shows good robustness under multiple working conditions, providing reliable technical support for the high-precision diagnosis of compound faults in locomotive axle box bearings and having significant engineering application value.</p>

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Compound Fault Diagnosis of High-Speed Train Axle Box Bearings Based on APSO-VMD and Adaptive Notch Filter

  • 期刊论文

  • 发表日期：

    2026年4月19日

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  • Jiandong Qiu,
  • Dingwang Zhang,
  • Minan Tang,
  • Meng Li,
  • Shutong Liu,
  • Jiaxuan Liu,
  • Jiaolong Wang,
  • Guangcan Lei

摘要

Aiming at the problems in the compound fault diagnosis of locomotive axle box bearings, where it is difficult to accurately separate and extract fault components, and the coupling effect between noise and compound fault components leads to misdiagnosis or missed diagnosis, this paper proposes an adaptive diagnosis method for compound faults in axle box bearings. First, the APSO algorithm is used to adaptively optimize the core parameters of VMD, effectively overcoming the limitation that traditional VMD parameters are set based on experience. Second, signal reconstruction is performed based on the correlation criterion between the intrinsic mode function (IMF) and the original signal, and the main fault of the bearing is identified through spectrum analysis. Then, after filtering out strong interference components, secondary fault features are extracted. Finally, through the synergistic effect of APSO-VMD and the adaptive notch filter, effective separation and accurate diagnosis of compound faults are achieved. MATLAB simulation and experimental data verification show that this method has good robustness and accuracy in bearing compound fault diagnosis, and the average relative error of its extracted characteristic frequencies is less than 4%. Meanwhile, this method shows good robustness under multiple working conditions, providing reliable technical support for the high-precision diagnosis of compound faults in locomotive axle box bearings and having significant engineering application value.