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Convolutional neural network framework for wind turbine electromechanical fault detection

Stone, Emilie; Giani, Stefano; Zappalá, Donatella; Crabtree, Christopher

Convolutional neural network framework for wind turbine electromechanical fault detection Thumbnail


Emilie Stone

Donatella Zappalá


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 realtime. 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.


Stone, E., Giani, S., Zappalá, D., & Crabtree, C. (2023). Convolutional neural network framework for wind turbine electromechanical fault detection. Wind Energy, 26(10), 1082 - 1097.

Journal Article Type Article
Acceptance Date Jul 6, 2023
Online Publication Date Aug 7, 2023
Publication Date 2023-10
Deposit Date Jul 7, 2023
Publicly Available Date Aug 22, 2023
Journal Wind Energy
Print ISSN 1095-4244
Electronic ISSN 1099-1824
Publisher Wiley Open Access
Peer Reviewed Peer Reviewed
Volume 26
Issue 10
Pages 1082 - 1097
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Published Journal Article (2.4 Mb)

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Copyright Statement
© 2023 The Authors. Wind Energy published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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