Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning

Meyer, Angela (2022). Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning Energies, 15(4), p. 1514. MDPI 10.3390/en15041514

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A growing number of wind turbines are equipped with vibration measurement systems to enable a 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 applicable also 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 the 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.

Item Type:	Journal Article (Original Article)
Division/Institute:	School of Engineering and Computer Science
Name:	Meyer, Angela0000-0003-4120-3827
ISSN:	1996-1073
Publisher:	MDPI
Language:	English
Submitter:	Angela Meyer
Date Deposited:	18 Nov 2022 12:19
Last Modified:	20 Nov 2022 01:36
Publisher DOI:	10.3390/en15041514
Uncontrolled Keywords:	Wind energy, fault detection and diagnosis, vibration-based condition monitoring, wind turbines, gearboxes, convolutional neural networks
ARBOR DOI:	10.24451/arbor.18002
URI:	https://arbor.bfh.ch/id/eprint/18002