Scholarlink

To enhance the robustness of morphological filtering-based bearing fault detection methods under complex noise interference, a performance-enhanced multi-order weighted time-varying morphological filtering is proposed. Firstly, to address the issue of traditional structural elements (SEs) being unable to accurately extract fault-related components from noisy vibration signals, a novel SE design strategy named multi-order time-varying SE is introduced, combining the advantages of multi-scale SEs and time-varying SEs, which can more accurately match and extract periodic transient features hidden in noisy signals. Subsequently, an information threshold is introduced into the filtered signals under time-varying SEs with different orders to construct a weighted function to enhance fault-related information and eliminate interference components. Finally, autocorrelation is applied to the weighted signal to further highlight fault-related features. The experimental results demonstrate that the proposed method can effectively extract bearing fault-related features and diagnose railway wheelset bearing faults, and its superiority is validated through comparison with existing methods.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Novel Time-Varying Structural Element for Morphological Filtering-Based Bearing Fault Diagnosis

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Shengbo Wang,
  • Xiaomo Jiang,
  • Bingyan Chen,
  • Haibin Yang,
  • Huaiyu Hui

摘要

To enhance the robustness of morphological filtering-based bearing fault detection methods under complex noise interference, a performance-enhanced multi-order weighted time-varying morphological filtering is proposed. Firstly, to address the issue of traditional structural elements (SEs) being unable to accurately extract fault-related components from noisy vibration signals, a novel SE design strategy named multi-order time-varying SE is introduced, combining the advantages of multi-scale SEs and time-varying SEs, which can more accurately match and extract periodic transient features hidden in noisy signals. Subsequently, an information threshold is introduced into the filtered signals under time-varying SEs with different orders to construct a weighted function to enhance fault-related information and eliminate interference components. Finally, autocorrelation is applied to the weighted signal to further highlight fault-related features. The experimental results demonstrate that the proposed method can effectively extract bearing fault-related features and diagnose railway wheelset bearing faults, and its superiority is validated through comparison with existing methods.