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The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

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  • Chao Zhang

    (School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Haoran Duan

    (School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Yu Xue

    (Beijing Tianrun New Energy Investment Co., Ltd., Beijing 100029, China)

  • Biao Zhang

    (School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Bin Fan

    (College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Jianguo Wang

    (School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Fengshou Gu

    (School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan.

Suggested Citation

  • Chao Zhang & Haoran Duan & Yu Xue & Biao Zhang & Bin Fan & Jianguo Wang & Fengshou Gu, 2020. "The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function," Energies, MDPI, vol. 13(23), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6348-:d:454664
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    References listed on IDEAS

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    1. Yunjiang Liu & Fuzhong Wang & Lu Liu & Yamin Zhu, 2019. "Symmetry tristable stochastic resonance induced by parameter under levy noise background," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(8), pages 1-8, August.
    2. Jianguo Wang & Minmin Xu & Chao Zhang & Baoshan Huang & Fengshou Gu, 2020. "Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis," Energies, MDPI, vol. 13(2), pages 1-17, January.
    3. Yuangen Yao & Lijian Yang & Canjun Wang & Quan Liu & Rong Gui & Juan Xiong & Ming Yi, 2018. "Subthreshold Periodic Signal Detection by Bounded Noise-Induced Resonance in the FitzHugh–Nagumo Neuron," Complexity, Hindawi, vol. 2018, pages 1-10, February.
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