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Unknown Bearing Fault Recognition in Strong Noise Background

  • ELECTROMAGNETIC METHODS
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Abstract

The fault pattern of rolling bearing usually is unknown in heavy noise background, which means the bearing has two possibilities of single part and compound fault. A new diagnosis method of bearing unknown fault is proposed, which can adaptively diagnose the bearing unknown fault with single part or multi-site. The ensemble empirical mode decomposition with the decomposition cut-off frequency is used to decompose noisy signals. Combined with the sample entropy index, a target frequency band screening criterion is structured to select the target frequency band that contains the potentially fault feature. Meanwhile, the advantage of sample entropy in anti-noise interference is analyzed. Subsequently, the spectral magnification is designed as the quantitative indicator to get the optimal system output of target frequency band when the fault frequency is unknown. Finally, the resonance factor is designed to eliminate the false fault caused by coherence resonance. The feasibility of the proposed approach is confirmed with both simulated and experimental signals.

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Funding

This research was supported by National Natural Science Foundation of China (grant no. 12072362) and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining and Technology, 221116, Xuzhou, China

    Chen Yang, Tao Gong & Jianhua Yang

  2. School of Mechanical Engineering, Dalian University of Technology, 116023, Dalian, China

    Chen Yang

  3. School of Computer Science and Technology, China University of Mining and Technology, 221116, Xuzhou, China

    Zhongqiu Wang

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  1. Chen Yang
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  2. Zhongqiu Wang
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  3. Tao Gong
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  4. Jianhua Yang
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Correspondence to Zhongqiu Wang.

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Yang, C., Wang, Z., Gong, T. et al. Unknown Bearing Fault Recognition in Strong Noise Background. Russ J Nondestruct Test 59, 560–582 (2023). https://doi.org/10.1134/S1061830923600016

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