Abstract
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.
Data availability
The data that support the findings of this research are available from the corresponding author upon reasonable request.
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https://mb.uni-paderborn.de/kat/forschung/kat-datacenter/bearing-datacenter/data-sets-and-download.
Acknowledgments
This work is supported by Zhejiang Transportation Investment Group Co., Ltd. Technology Plan Project (202403).
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Qingshou Zheng , Yanwei Li, Youwen Chen, Xing Chen, Ke Xu, Zihong Yin wrote the main manuscript text and reviewed the manuscript.
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Zheng, Q., Li, Y., Chen, Y. et al. Fault diagnosis method of rolling-element bearings via FBEWT and EIRVM. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44164-6
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DOI: https://doi.org/10.1038/s41598-026-44164-6
