Convolutional neural network framework for wind turbine electromechanical fault detection - Emilie Stone; Stefano Giani; Donatella Zappalá; Christopher Crabtree (Wind Energy, 2023)
Abstract Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high‐dimensional raw condition monitoring data for the automatic detection of multiple wind turbine electromechanical faults. The proposed approach leverages the potential of combining information from a variety of signals to learn features and to discriminate the types of fault and their severity. As a result of the CNN layers used to extract features from the signals, this architecture works in the time domain and can digest high‐resolution multi‐sensor data streams in real‐time. To overcome the inherent black‐box nature of AI models, this research proposes two interpretability techniques, multidimensional scaling and layer‐wise relevance propagation, to analyse the proposed model's inner‐working and identify the signal features relevant for fault classification. Experimental results show high performance and classification accuracies above 99.9% for all fault cases tested, demonstrating the efficacy of the proposed fault‐detection system.