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Article

Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning

School of Engineering and Computer Science, Bern University of Applied Sciences, 2501 Biel, Switzerland
Academic Editor: Mohamed Benbouzid
Energies 2022, 15(4), 1514; https://doi.org/10.3390/en15041514
Received: 24 December 2021 / Revised: 10 February 2022 / Accepted: 16 February 2022 / Published: 18 February 2022
(This article belongs to the Collection Women's Research in Wind and Ocean Energy)
A growing number of wind turbines are equipped with vibration measurement systems to enable the close monitoring and early detection of developing fault conditions. The vibration measurements are analyzed to continuously assess the component health and prevent failures that can result in downtimes. This study focuses on gearbox monitoring but is also applicable to other subsystems. The current state-of-the-art gearbox fault diagnosis algorithms rely on statistical or machine learning methods based on fault signatures that have been defined by human analysts. This has multiple disadvantages. Defining the fault signatures by human analysts is a time-intensive process that requires highly detailed knowledge of gearbox composition. This effort needs to be repeated for every new turbine, so it does not scale well with the increasing number of monitored turbines, especially in fast-growing portfolios. Moreover, fault signatures defined by human analysts can result in biased and imprecise decision boundaries that lead to imprecise and uncertain fault diagnosis decisions. We present a novel accurate fault diagnosis method for vibration-monitored wind turbine components that overcomes these disadvantages. Our approach combines autonomous data-driven learning of fault signatures and health state classification based on convolutional neural networks and isolation forests. We demonstrate its performance with vibration measurements from two wind turbine gearboxes. Unlike the state-of-the-art methods, our approach does not require gearbox-type-specific diagnosis expertise and is not restricted to predefined frequencies or spectral ranges but can monitor the full spectrum at once. View Full-Text
Keywords: wind energy; fault detection and diagnosis; vibration-based condition monitoring; wind turbines; gearboxes; convolutional neural networks wind energy; fault detection and diagnosis; vibration-based condition monitoring; wind turbines; gearboxes; convolutional neural networks
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MDPI and ACS Style

Meyer, A. Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning. Energies 2022, 15, 1514. https://doi.org/10.3390/en15041514

AMA Style

Meyer A. Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning. Energies. 2022; 15(4):1514. https://doi.org/10.3390/en15041514

Chicago/Turabian Style

Meyer, Angela. 2022. "Vibration Fault Diagnosis in Wind Turbines Based on Automated Feature Learning" Energies 15, no. 4: 1514. https://doi.org/10.3390/en15041514

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