A deep capsule neural network with data augmentation generative adversarial networks for single and simultaneous fault diagnosis of wind turbine gearbox. (April 2023) - Liang, Pengfei; Deng, Chao; Yuan, Xiaoming; Zhang, Lijie
Abstract: The fault diagnosis (FD) of wind turbine gearbox (WTG) is of special importance for keeping the wind turbine drivetrain working normally and safely. However, owing to the limited training data and the mutual interference of various mechanical parts, it is of great difficulty to realize the simultaneous-fault monitoring task of WTG using existing intelligent FD methods or manual inspection-based approaches. To tackle the issue, a deep capsule neural network with data augmentation generative adversarial networks, named ST-DAGANs-CapNet, is developed for the single and simultaneous FD of WTG by integrating capsule neural network (CapsNet) with Stockwell transform (ST) and data augmentation generative adversarial networks (DAGANs). The proposed ST-DAGANs-CapNet method mainly consists of three steps. First of all, ST is adopted to extract two-dimension (2-d) image features of time–frequency domain from raw time-domain vibration signals of WTG. Then, DAGANs are employed for generating more fake image samples to address the problem of lacking training data. At last, the built CapsNet model is utilized to diagnose the single and compound faults of WTG by the primary 2-d feature images and the made fake 2-d feature images in training set. Two experimental studies are implemented to prove the effectiveness of the proposed method, and the result is compared with some existing intelligent FD of WTG. It indicates that DAGANs are effective in helping to tackle the issue of limited and unbalanced training samples in real FD of WTG, and the diagnosis result of the proposed approach in test sample set is better than that of several commonly used FD methods in literatures. Highlights: A novel intelligent fault diagnosis method named ST-DAGANs-CapsNet is proposed for the wind turbine gearbox. DAGANs are used to make more training samples to address the issue of limited and unbalanced training data. The structure of DAGANs is improved by adding an additional generator to solve the challenge of mode collapse in original GANs.