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Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity

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  • Yan, Xiaoan
  • Liu, Ying
  • Xu, Yadong
  • Jia, Minping

Abstract

When wind turbine driving system (WTDS) undergoes abnormal conditions, the fault information hidden in WTDS scatters over multiple signal channels and hence inadequate for fault diagnosis only via fault information extraction of single-channel signal. To make full use of multichannel fault information of WTDS and improve diagnostic accuracy, this paper proposes a new approach based on multivariate singular spectrum decomposition (MSSD) and improved Kolmogorov complexity (IKC). Firstly, based on singular spectrum decomposition (SSD) and the idea of multichannel data processing, a multivariate singular spectrum decomposition (MSSD) method is presented to process multichannel vibration data collected from WTDS, which can obtain adaptively multichannel mode components without extra user-defined parameters. Secondly, through incorporating symbolization process into Kolmogorov complexity (KC), an improved complexity metric abbreviated as IKC is proposed to capture the fault information of multichannel mode components, which can enhance fault feature extraction ability of KC. Finally, IKC-based multichannel fault features are fed into partial least squares (PLS) to automatically discriminate different fault patterns of WTDS. Practical engineering data from WTDS demonstrate the effectiveness of the proposed approach. Additionally, the superiority of the proposed approach has also proven in extracting fault information and health condition identification compared to the other multichannel methods and traditional single-channel approaches reported in the literature.

Suggested Citation

  • Yan, Xiaoan & Liu, Ying & Xu, Yadong & Jia, Minping, 2021. "Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity," Renewable Energy, Elsevier, vol. 170(C), pages 724-748.
    • Handle: RePEc:eee:renene:v:170:y:2021:i:c:p:724-748
    DOI: 10.1016/j.renene.2021.02.011
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    Citations

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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. Liu, Dongdong & Cui, Lingli & Cheng, Weidong, 2023. "Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation," Renewable Energy, Elsevier, vol. 206(C), pages 645-657.
    3. Dibaj, Ali & Gao, Zhen & Nejad, Amir R., 2023. "Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements," Renewable Energy, Elsevier, vol. 203(C), pages 161-176.
    4. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Yunhai Song & Sen He & Liwei Wang & Zhenzhen Zhou & Yuhao He & Yaohui Xiao & Yi Zheng & Yunfeng Yan, 2023. "Anomaly Perception Method of Substation Scene Based on High-Resolution Network and Difficult Sample Mining," Sustainability, MDPI, vol. 15(18), pages 1-13, September.
    6. Shao, Kaixuan & He, Yigang & Xing, Zhikai & Du, Bolun, 2023. "Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples," Reliability Engineering and System Safety, Elsevier, vol. 233(C).

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