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Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

Author

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  • Lei Fu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Tiantian Zhu

    (College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China)

  • Kai Zhu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yiling Yang

    (Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China)

Abstract

Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

Suggested Citation

  • Lei Fu & Tiantian Zhu & Kai Zhu & Yiling Yang, 2019. "Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy," Energies, MDPI, vol. 12(16), pages 1-20, August.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3085-:d:256520
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    References listed on IDEAS

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    1. Miao He & David He & Jae Yoon & Thomas J Nostrand & Junda Zhu & Eric Bechhoefer, 2019. "Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach," Journal of Risk and Reliability, , vol. 233(3), pages 303-316, June.
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    5. Yang, Dong & Li, Hui & Hu, Yaogang & Zhao, Jie & Xiao, Hongwei & Lan, Yongsen, 2016. "Vibration condition monitoring system for wind turbine bearings based on noise suppression with multi-point data fusion," Renewable Energy, Elsevier, vol. 92(C), pages 104-116.
    6. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
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    Cited by:

    1. Maria Rosaria Termite & Piero Baraldi & Sameer Al-Dahidi & Luca Bellani & Michele Compare & Enrico Zio, 2019. "A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments," Energies, MDPI, vol. 12(24), pages 1-26, December.
    2. Xihui Chen & Aimin Ji & Gang Cheng, 2019. "A Novel Deep Feature Learning Method Based on the Fused-Stacked AEs for Planetary Gear Fault Diagnosis," Energies, MDPI, vol. 12(23), pages 1-18, November.
    3. Isac Antônio dos Santos Areias & Luiz Eduardo Borges da Silva & Erik Leandro Bonaldi & Levy Ely de Lacerda de Oliveira & Germano Lambert-Torres & Vitor Almeida Bernardes, 2019. "Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the Vibration in Induction Motors," Energies, MDPI, vol. 12(21), pages 1-15, October.
    4. Yolanda Vidal, 2023. "Artificial Intelligence for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 16(4), pages 1-4, February.
    5. Lei Fu & Yiling Yang & Xiaolong Yao & Xufen Jiao & Tiantian Zhu, 2019. "A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion," Energies, MDPI, vol. 12(20), pages 1-23, October.

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