DeepFedWT: A federated deep learning framework for fault detection of wind turbines. (August 2022)

• Record Type: Journal Article
• Authors: Jiang, Guoqian; Fan, WeiPeng; Li, Wenyue; Wang, Lijin; He, Qun; Xie, Ping; Li, Xiaoli
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Federated learning -- Deep learning -- Multi-scale spatial feature extraction -- Feature attention -- Fault detection -- Wind turbines (WTs); Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111529
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Data-driven fault detection of wind turbines has gained increasingly attention. Currently, most existing methods require sufficient labeled data to train a reliable model in a centralized way. However, it is difficult to collect enough labeled data due to data privacy and strict confidentiality of wind farm owners. To this end, we propose a federated deep learning framework (DeepFedWT), which allows multiple decentralized WTs to collaboratively build a fault detection model using their local private data. Specifically, we designed a multi-scale residual attention network (MSRAN) model to extract informative features from raw multivariate sensor data, which first integrates a multiscale residual learning block to extract spatial features among different sensor variables at multiple scales and adopts a feature attention block to highlight important features highly associated with faults, and finally enables an enhanced fault detection. Experimental results on two real WT datasets demonstrate the effectiveness of our proposed DeepFedWT framework. Highlights: A federated deep learning framework is proposed for collaborative WT fault detection. An MSRAN model is proposed to highlight important features from SCADA data. The proposed method is evaluated two real SCADA datasets. The proposed DeepFedWT obtained improved performance than the local learning model.