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Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors

Author

Listed:
  • Xu, Zifei
  • Mei, Xuan
  • Wang, Xinyu
  • Yue, Minnan
  • Jin, Jiangtao
  • Yang, Yang
  • Li, Chun

Abstract

In order to solve the problems of insufficient extrapolation of intelligent models for the fault diagnosis of bearings in real wind turbines, this study has developed a multi-scale convolutional neural network with bidirectional long short term memory (MSCNN-BiLSTM) model for improving the generalization abilities under complex working and testing environments. A weighted majority voting rule has been proposed to fuse the information from multi-sensors for improving the extrapolation of multisensory diagnosis. The superiority of the MSCNN-BiLSTM model is examined through experimental data. The results indicate that the MSCNN-BiLSTM model has 97.12% mean F1 score, which is higher than existing advanced methods. Real wind turbine dataset and an experimental dataset are used to demonstrate the effectiveness of the weighted majority voting rule for multisensory diagnosis. The results present that the diagnosis result of the MSCNN-BiLSTM model with weighted majority voting rule is higher respectively 1.32% and 5.7% than the model with traditional majority voting or fusion of multisensory information in feature-level.

Suggested Citation

  • Xu, Zifei & Mei, Xuan & Wang, Xinyu & Yue, Minnan & Jin, Jiangtao & Yang, Yang & Li, Chun, 2022. "Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors," Renewable Energy, Elsevier, vol. 182(C), pages 615-626.
    • Handle: RePEc:eee:renene:v:182:y:2022:i:c:p:615-626
    DOI: 10.1016/j.renene.2021.10.024
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    References listed on IDEAS

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    1. Chen, Jinglong & Pan, Jun & Li, Zipeng & Zi, Yanyang & Chen, Xuefeng, 2016. "Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals," Renewable Energy, Elsevier, vol. 89(C), pages 80-92.
    2. Lin, Zhongwei & Chen, Zhenyu & Wu, Qiuwei & Yang, Shuo & Meng, Hongmin, 2018. "Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis," Energy, Elsevier, vol. 147(C), pages 812-825.
    3. Zuo, Hongyan & Tan, Jiqiu & Wei, Kexiang & Huang, Zhonghua & Zhong, Dingqing & Xie, Fuchun, 2021. "Effects of different poses and wind speeds on wind-induced vibration characteristics of a dish solar concentrator system," Renewable Energy, Elsevier, vol. 168(C), pages 1308-1326.
    4. Chang, Yuanhong & Chen, Jinglong & Qu, Cheng & Pan, Tongyang, 2020. "Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels," Renewable Energy, Elsevier, vol. 153(C), pages 205-213.
    5. Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
    6. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
    7. Teng, Wei & Ding, Xian & Cheng, Hao & Han, Chen & Liu, Yibing & Mu, Haihua, 2019. "Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform," Renewable Energy, Elsevier, vol. 136(C), pages 393-402.
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    2. Xie, Tianming & Xu, Qifa & Jiang, Cuixia & Lu, Shixiang & Wang, Xiangxiang, 2023. "The fault frequency priors fusion deep learning framework with application to fault diagnosis of offshore wind turbines," Renewable Energy, Elsevier, vol. 202(C), pages 143-153.
    3. Guo, Junyu & Yang, Yulai & Li, He & Wang, Jiang & Tang, Aimin & Shan, Daiwei & Huang, Bangkui, 2024. "A hybrid deep learning model towards fault diagnosis of drilling pump," Applied Energy, Elsevier, vol. 372(C).
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    5. Lin Wang & Fangqing Zhang & Jiefei Wang & Gang Ren & Dengxian Wang & Ling Gao & Xingyu Ming, 2024. "Fault Diagnosis Method for Hydropower Station Measurement and Control System Based on ISSA-VMD and 1DCNN-BiLSTM," Energies, MDPI, vol. 17(22), pages 1-25, November.

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