Fault diagnosis for induction generator-based wind turbine using ensemble deep learning techniques

Abstract

Fault diagnosis in induction machines (IM) require effective detection at early stages to prevent permanent machine failure. This requires fast sensing of disturbances as well as efficient diagnosis methods. Conventional signal measuring techniques are usually intrusive, sensitive, with a low signal-to-noise ratio, and may show limited performance at incipient fault stages. Moreover, traditional machine learning (ML) diagnosis techniques based on handcrafted feature extraction methods suffer from limitations that can be overcome by deep learning (DL) with automatic feature extraction capabilities. In this paper, an online, non-intrusive method is proposed for fault diagnosis in a three-phase IM based on infrared imaging. The proposed method integrates three convolutional neural network (CNN)-based DL models (Inception, Xception, and MobileNet) to identify IM health status, fault types, and stator Inter-turn faults (ITF) location and severity. Deep features obtained from these CNNs are merged through Discrete Wavelet Transform (DWT) resulting in spatial-time-frequency features. This fusion reduces the input features’ size and improves diagnostic accuracy. Merged features’ length is further reduced using the Principal Component Analysis (PCA) where these reduced features are then used for classification. Compared to DL-based diagnosis methods introduced in previous work, the proposed method shows superior performance. First, it uses the IR images directly without the need for clustering and segmentation steps, thus saving time and effort. Moreover, ensembled DL is applied via combining the three CNNs benefits altogether rather than applying an individual DL method. Finally, unlike the huge features’ number exhibited by previous studies, the proposed approach uses minimal features, thus reducing classification complexity and time while maintaining 100% classification accuracy.