A transferable diagnosis method with incipient fault detection for a digital twin of wind turbine

Accurate and transferable fault diagnosis methods have a critical role in constructing a digital twin (DT) of wind turbine (WT). These methods can be utilized to predict premature failures and to maintain a stable power supply. However, the infeasibility of obtaining enough degradation data with various failure mechanisms is one of major problems for WTs. This paper proposes a fault detection and diagnosis method for WTs using tools, including average integrated power spectral density (AIPSD), one-class support vector machine (OCSVM), and neural architecture optimization (NAO)-CapsNet, based on limited data. Moreover, a WT DT framework is designed for real-time condition monitoring of the WT during its lifecycle. Datasets collected from real-world WT gearboxes and pitch bearings are selected to verify the performance of the proposed method. The experimental results show that the developed method can not only trace when the fault occurred, but also obtain better diagnosis accuracy compared with other state-of-the-art methods. In addition, the proposed NAO-CapsNet trained by dataset from two WTs can be successfully transferred and applied to DT-based condition monitoring of another WT.