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One-Shot Fault Diagnosis of Wind Turbines Based on Meta-Analogical Momentum Contrast Learning

Author

Listed:
  • Xiaobo Liu

    (Key Laboratory of Power Station Energy Transfer Conversion and System, North China Electric Power University, Ministry of Education, Beijing 102206, China)

  • Hantao Guo

    (Key Laboratory of Power Station Energy Transfer Conversion and System, North China Electric Power University, Ministry of Education, Beijing 102206, China)

  • Yibing Liu

    (Key Laboratory of Power Station Energy Transfer Conversion and System, North China Electric Power University, Ministry of Education, Beijing 102206, China)

Abstract

The rapid development of artificial intelligence offers more opportunities for intelligent mechanical diagnosis. Fault diagnosis of wind turbines is beneficial to improve the reliability of wind turbines. Due to various reasons, such as difficulty in obtaining fault data, random changes in operating conditions, or compound faults, many deep learning algorithms show poor performance. When fault samples are small, ordinary deep learning will fall into overfitting. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples. A novel method based on meta-analogical momentum contrast learning (MA-MOCO) is proposed in this paper to solve the problem of the very few samples of wind turbine failures, especially one-shot. By improving the momentum contrast learning (MOCO) and using the training idea of meta-learning, the one-shot fault diagnosis of wind turbine drivetrain is analyzed. The proposed model shows a higher accuracy than other common models (e.g., model-agnostic meta-learning and Siamese net) in one-shot learning. The feature embedding is visualized by t-distributed stochastic neighbor embedding (t-SNE) in order to test the effectiveness of the proposed model.

Suggested Citation

  • Xiaobo Liu & Hantao Guo & Yibing Liu, 2022. "One-Shot Fault Diagnosis of Wind Turbines Based on Meta-Analogical Momentum Contrast Learning," Energies, MDPI, vol. 15(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3133-:d:801799
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    References listed on IDEAS

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    1. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
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    Cited by:

    1. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.

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