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Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal

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  • Miao, Yonghao
  • Zhao, Ming
  • Liang, Kaixuan
  • Lin, Jing

Abstract

Due to severe working condition, unexpected failures in wind turbine gearbox become rather frequent and may lead to long downtime or even catastrophic casualties. However, traditional diagnosis techniques based on vibration, acoustic emission etc. still face some problems when they are used for failure identification of wind turbine gearbox. Encoder signal carries rich diagnostic information which may be considered as an alternative tool for the wind turbine condition monitoring. Motivated by this, the encoder signal is initially introduced for the fault diagnosis of wind turbine gear in this paper. A novel adaptive filtering method, improved maximum correlated kurtosis deconvolution adjusted (IMCKDA), is proposed to eliminate the diverse noises in encoder signal. Additionally, to overcome the limitation from the sensibility of discontinuity point and filtered signal in traditional deconvolution methods (DMs), convolution adjustment definition is introduced. And correlated Gini index (CG) is originally designed to guide the selection of filter length. Finally, the encoder signal is verified to be an alternative tool for the fault diagnosis of wind turbine gear by real experimental cases. And without any prior knowledge and the least input parameters, IMCKDA is more suitable for processing encoder signal than existing state-of-the-art DMs.

Suggested Citation

  • Miao, Yonghao & Zhao, Ming & Liang, Kaixuan & Lin, Jing, 2020. "Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal," Renewable Energy, Elsevier, vol. 151(C), pages 192-203.
    • Handle: RePEc:eee:renene:v:151:y:2020:i:c:p:192-203
    DOI: 10.1016/j.renene.2019.11.012
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    References listed on IDEAS

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    Cited by:

    1. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
    2. Wang, Jingjing & Miao, Yonghao, 2021. "Optimal preventive maintenance policy of the balanced system under the semi-Markov model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.

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