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The fault frequency priors fusion deep learning framework with application to fault diagnosis of offshore wind turbines

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  • Xie, Tianming
  • Xu, Qifa
  • Jiang, Cuixia
  • Lu, Shixiang
  • Wang, Xiangxiang

Abstract

In fault diagnosis, deep learning plays an important role, but still lacks good interpretability. To address this issue, we develop a novel fault frequency priors fusion deep learning (FFP-DL) framework by introducing fault frequency priors into deep learning. The FFP-DL framework contains two branches: fault frequency priors learning branch (FFPLB) and self-learning branch (SLB). We then propose a pre-training algorithm which can shorten the overall training time especially for training multiple models simultaneously. To illustrate its efficacy, we take convolutional neural network (CNN) as the specific deep learning model in the FFP-DL framework (FFP-CNN), and apply the FFP-CNN model to a private offshore wind turbines (OWTs) data. The experimental results show that the FFP fusion does help improve the performance of fault diagnosis in terms of accuracy and Marco-F1-score and provide good interpretability to the diagnosis results with the distinguished feature of predicted FFP. With the training data reduction, the performance of the FFP-CNN model does not deteriorate quickly, which implies that this framework is also suitable for less data. In addition, the result reveals the fact that the pre-training algorithm does reduce convergence epochs, which will help the FFP-CNN model train faster during the training process.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:202:y:2023:i:c:p:143-153
    DOI: 10.1016/j.renene.2022.11.064
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    References listed on IDEAS

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    1. Qifa Xu & Shixiang Lu & Weiyin Jia & Cuixia Jiang, 2020. "Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1467-1481, August.
    2. Sakaris, Christos S. & Yang, Yang & Bashir, Musa & Michailides, Constantine & Wang, Jin & Sakellariou, John S. & Li, Chun, 2021. "Structural health monitoring of tendons in a multibody floating offshore wind turbine under varying environmental and operating conditions," Renewable Energy, Elsevier, vol. 179(C), pages 1897-1914.
    3. Rahimilarki, Reihane & Gao, Zhiwei & Jin, Nanlin & Zhang, Aihua, 2022. "Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine," Renewable Energy, Elsevier, vol. 185(C), pages 916-931.
    4. 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.
    5. 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.
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    Cited by:

    1. Jin, Zhenglei & Xu, Qifa & Jiang, Cuixia & Wang, Xiangxiang & Chen, Hao, 2023. "Ordinal few-shot learning with applications to fault diagnosis of offshore wind turbines," Renewable Energy, Elsevier, vol. 206(C), pages 1158-1169.
    2. Zhao, Zhigao & Chen, Fei & Gui, Zhonghua & Liu, Dong & Yang, Jiandong, 2023. "Refined composite hierarchical multiscale Lempel-Ziv complexity: A quantitative diagnostic method of multi-feature fusion for rotating energy devices," Renewable Energy, Elsevier, vol. 218(C).
    3. Qian, XiaoYi & Sun, TianHe & Zhang, YuXian & Wang, BaoShi & Awad Gendeel, Mohammed Altayeb, 2023. "Wind turbine fault detection based on spatial-temporal feature and neighbor operation state," Renewable Energy, Elsevier, vol. 219(P1).
    4. 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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