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

• Published in: Renewable energy 2023-01, Vol.202, p.143-153
• Main Authors: Xie, Tianming, Xu, Qifa, Jiang, Cuixia, Lu, Shixiang, Wang, Xiangxiang
• Format: Article
• Language: English
• Subjects: Deep learning; Fault diagnosis; Fault frequency; FFP-DL; Offshore wind turbines; Priors fusion
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2022.11.064

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. •The FFP-DL framework is proposed by adding fault frequency priors to deep learning.•It is a flexible framework by considering different deep learning structures.•It contains two branches for both frequency priors learning and self-learning.•Its efficacy has been demonstrated on the fault diagnosis of offshore WTs.•The empirical results show that the FFP fusion does make sense in fault diagnosis.