Vibration Fault Detection in Wind Turbines Based on Normal Behaviour Models without Feature Engineering

Stefan Jonas (), Dimitrios Anagnostos, Bernhard Brodbeck and Angela Meyer
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Stefan Jonas: School of Engineering and Computer Science, Bern University of Applied Sciences, Quellgasse 21, 2501 Biel, Switzerland
Dimitrios Anagnostos: WinJi AG, Badenerstrasse 808, 8048 Zurich, Switzerland
Bernhard Brodbeck: WinJi AG, Badenerstrasse 808, 8048 Zurich, Switzerland
Angela Meyer: School of Engineering and Computer Science, Bern University of Applied Sciences, Quellgasse 21, 2501 Biel, Switzerland

Energies, 2023, vol. 16, issue 4, 1-16

Abstract: Most wind turbines are remotely monitored 24/7 to allow for early detection of operation problems and developing damage. We present a new fault detection approach for vibration-monitored drivetrains that does not require any feature engineering. Our method relies on a simple model architecture to enable a straightforward implementation in practice. We propose to apply convolutional autoencoders for identifying and extracting the most relevant features from a broad continuous range of the spectrum in an automated manner, saving time and effort. We focus on the range of [0, 1000] Hz for demonstration purposes. A spectral model of the normal vibration response is learnt for the monitored component from past measurements. We demonstrate that the trained model can successfully distinguish damaged from healthy components and detect a damaged generator bearing and damaged gearbox parts from their vibration responses. Using measurements from commercial wind turbines and a test rig, we show that vibration-based fault detection in wind turbine drivetrains can be performed without the usual upfront definition of spectral features. Another advantage of the presented method is that a broad continuous range of the spectrum can be monitored instead of the usual focus on monitoring individual frequencies and harmonics. Future research should investigate the proposed method on more comprehensive datasets and fault types.

Keywords: condition monitoring; wind turbines; fault detection; vibrations; autoencoders; convolutional autoencoders; neural networks; renewable energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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