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Fault Detection of Wind Turbine Blades Using Multi-Channel CNN

Author

Listed:
  • Meng-Hui Wang

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Shiue-Der Lu

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Cheng-Che Hsieh

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Chun-Chun Hung

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

Abstract

This study utilized the multi-channel convolutional neural network (MCNN) and applied it to wind turbine blade and blade angle fault detection. The proposed approach automatically and effectively captures fault characteristics from the imported original vibration signals and identifies their state in multiple convolutional neural network (CNN) models. The result obtained from each model is sent to the output layer, which is a maximum output network (MAXNET), to compute the most accurate state. First, in terms of wind turbine blade state detection, this paper builds blade models based on the normal state and three common fault types, including blade angle anomaly, blade surface damage, and blade breakage. Vibration signals are employed for fault detection. The proposed wind turbine fault diagnosis approach adopts a triaxial vibration transducer and frame grabber to capture vibration signals and then applies the new MCNN algorithm to identify the state. The test results show that the proposed approach could deliver up to 87.8% identification accuracy for four fault types of large wind turbine blades.

Suggested Citation

  • Meng-Hui Wang & Shiue-Der Lu & Cheng-Che Hsieh & Chun-Chun Hung, 2022. "Fault Detection of Wind Turbine Blades Using Multi-Channel CNN," Sustainability, MDPI, vol. 14(3), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1781-:d:742007
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    Citations

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    Cited by:

    1. K. Ramakrishna Kini & Fouzi Harrou & Muddu Madakyaru & Ying Sun, 2023. "Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis," Energies, MDPI, vol. 16(15), pages 1-25, August.
    2. Baiyun Qian & Jinjun Huang & Xiaoxun Zhu & Ruijun Wang & Xiang Lin & Ning Gao & Wei Li & Lijiang Dong & Wei Liu, 2022. "Research on the Fault Diagnosis Method of a Synchronous Condenser Based on the Multi-Scale Zooming Learning Framework," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    3. Saud Altaf & Shafiq Ahmad & Mazen Zaindin & Shamsul Huda & Sofia Iqbal & Muhammad Waseem Soomro, 2022. "Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network," Sustainability, MDPI, vol. 14(16), pages 1-29, August.
    4. Wangpeng He & Peipei Zhang & Xuan Liu & Binqiang Chen & Baolong Guo, 2022. "Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis," Sustainability, MDPI, vol. 14(24), pages 1-15, December.

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