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A novel wind turbine fault diagnosis method based on compressed sensing and DTL-CNN

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  • Zhang, Yan
  • Liu, Wenyi
  • Wang, Xin
  • Gu, Heng

Abstract

This paper describes the development of a fault diagnosis method for identifying different fault conditions in the rolling bearings and gears of wind turbines. For the fault signal, the compressed sensing (CS) technology is used to perform noise reduction and feature extraction. The noise reduction process consists of sparse compression and reconstruction of the signal. After the data is processed by the compressed sensing technology, the noise and redundant parts of the signal can be greatly removed, and the real operating state signal of the wind turbine can be restored to the maximum. The fault diagnosis scheme is based on a combination of deep transfer learning and convolutional neural network (DTL-CNN), which is able to perform fault type identification with a small batch of rolling bearing data samples and gear samples. In this study, a new CNN structure was developed and the structure was used to achieve bearing-to-bearing and bearing-to-gear transfer fault diagnosis. Finally, the reliability and superiority of the proposed method in wind turbine rolling bearing and gear fault diagnosis are shown by the experimental results.

Suggested Citation

  • Zhang, Yan & Liu, Wenyi & Wang, Xin & Gu, Heng, 2022. "A novel wind turbine fault diagnosis method based on compressed sensing and DTL-CNN," Renewable Energy, Elsevier, vol. 194(C), pages 249-258.
    • Handle: RePEc:eee:renene:v:194:y:2022:i:c:p:249-258
    DOI: 10.1016/j.renene.2022.05.085
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    References listed on IDEAS

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    1. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    2. Gao, Q.W. & Liu, W.Y. & Tang, B.P. & Li, G.J., 2018. "A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine," Renewable Energy, Elsevier, vol. 116(PA), pages 169-175.
    3. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    4. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
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    1. Zemali, Zakaria & Cherroun, Lakhmissi & Hadroug, Nadji & Hafaifa, Ahmed & Iratni, Abdelhamid & Alshammari, Obaid S. & Colak, Ilhami, 2023. "Robust intelligent fault diagnosis strategy using Kalman observers and neuro-fuzzy systems for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 205(C), pages 873-898.

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