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Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis

Author

Listed:
  • Jianguo Wang

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

  • Minmin Xu

    (State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
    Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Chao Zhang

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

  • Baoshan Huang

    (School of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, China)

  • Fengshou Gu

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
    School of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, China)

Abstract

Accurate diagnosis of incipient faults in wind turbine (WT) assets will provide sufficient lead time to apply an optimal maintenance for the expensive WT assets which often are located in a remote and harsh environment and their maintenance usually needs heavy equipment and highly skilled engineers. This paper presents an online bearing clearance monitoring approach to diagnose the change of bearing clearance, providing an early and interpretable indication of bearing health conditions. A novel dynamic load distribution method is developed to efficiently gain the general characteristics of vibration response of bearings without local defects but with small geometric errors. It shows that the ball pass frequency of outer race (BPFO) is the primary exciting source due to biased load distribution relating to bearing clearance. The geometric errors, including various orders of runouts on different bearing parts, can be the secondary excitation source. Both sources lead to compound modulation responses with very low amplitudes, being more than 20 dB lower than that of a small local defect on raceways and often buried by background noise. Then, Modulation Signal Bispectrum (MSB) is identified to purify the noisy signal and Gini index is introduced to represent the peakness of MSB results, thereby an interpretable indicator bounded between 0 and 1 is established to show bearing clearance status. Datasets from both a dedicated bearing test and a run-to-failure gearbox test are employed to verify the performance and reliability of the proposed approach. Results show that the proposed method is capable to indicate a change of about 20 µm in bearing clearance online, which provides a significantly long lead time compared to the diagnosis method that focuses only on local defects. Therefore, this method provides a big opportunity to implement more cost-effective maintenance works or carry out timely remedial actions to prolong the lifespan of bearings. Obviously, it is applicable to not only WT assets, but also most rotating machines.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:389-:d:308204
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    References listed on IDEAS

    as
    1. Wang, Jinjiang & Liang, Yuanyuan & Zheng, Yinghao & Gao, Robert X. & Zhang, Fengli, 2020. "An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples," Renewable Energy, Elsevier, vol. 145(C), pages 642-650.
    2. Baoshan Huang & Guojin Feng & Xiaoli Tang & James Xi Gu & Guanghua Xu & Robert Cattley & Fengshou Gu & Andrew D. Ball, 2019. "A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems," Energies, MDPI, vol. 12(8), pages 1-23, April.
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    Cited by:

    1. Akilu Yunusa-Kaltungo & Ruifeng Cao, 2020. "Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults," Energies, MDPI, vol. 13(6), pages 1-20, March.
    2. Xiaohua Song & Jing Liu & Chaobo Chen & Song Gao, 2022. "Advanced Methods in Rotating Machines," Energies, MDPI, vol. 15(15), pages 1-3, July.
    3. 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.

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