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<p>Prompt and accurate detection of rolling-element-bearing defects is essential for the reliable operation of rotating equipment. This study presents an enhanced diagnostic strategy that couples Fourier–Bessel empirical wavelet transform (FBEWT) with an enhanced incremental relevance vector machine (EIRVM). FBEWT’s core novelty lies in using amplitude-modulation information to preset its frequency boundaries, yielding stable results even when sampling rate or observation window changes. Meanwhile, the upgraded incremental RVM handles the final classification of bearing health states. Test data show that FBEWT outperforms conventional empirical wavelet decomposition, and EIRVM surpasses the standard RVM classifier. Overall, the integrated FBEWT–EIRVM framework demonstrates strong capability for diagnosing rolling-element-bearing faults.</p>

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Fault diagnosis method of rolling-element bearings via FBEWT and EIRVM

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月22日

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  • Qingshou Zheng,
  • Yanwei Li,
  • Youwen Chen,
  • Xing Chen,
  • Ke Xu,
  • Zihong Yin

摘要

Prompt and accurate detection of rolling-element-bearing defects is essential for the reliable operation of rotating equipment. This study presents an enhanced diagnostic strategy that couples Fourier–Bessel empirical wavelet transform (FBEWT) with an enhanced incremental relevance vector machine (EIRVM). FBEWT’s core novelty lies in using amplitude-modulation information to preset its frequency boundaries, yielding stable results even when sampling rate or observation window changes. Meanwhile, the upgraded incremental RVM handles the final classification of bearing health states. Test data show that FBEWT outperforms conventional empirical wavelet decomposition, and EIRVM surpasses the standard RVM classifier. Overall, the integrated FBEWT–EIRVM framework demonstrates strong capability for diagnosing rolling-element-bearing faults.