Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach

Waqar Ali (), Idriss El-Thalji, Knut Erik Teigen Giljarhus and Andreas Delimitis
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Waqar Ali: Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, 4021 Stavanger, Norway
Idriss El-Thalji: Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, 4021 Stavanger, Norway
Knut Erik Teigen Giljarhus: Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, 4021 Stavanger, Norway
Andreas Delimitis: Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, 4021 Stavanger, Norway

Energies, 2024, vol. 17, issue 23, 1-27

Abstract: Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have used time-domain and frequency-domain features alongside machine learning techniques to predict faults in wind turbine blades, such as erosion and cracks. However, a key gap remains in integrating these methods into a unified framework for fault prediction, which could offer a more comprehensive solution for diagnosing faults. This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. Open-source vibration data collected from an experimental setup (where a small wind turbine with an artificially eroded and cracked blade was tested) were utilized. The time- and frequency-domain features were extracted and analyzed using various machine learning algorithms. It was found that erosion and crack faults have unique time- and frequency-domain features. The crack fault introduces an amplitude modulation in the vibration time wave, which produces sidebands around the fundamental frequency in the frequency domain. However, erosion fault introduces asymmetricity and flatness to the vibration time wave, which produces harmonics in the frequency-domain plot. The results also highlighted that utilizing both time- and frequency-fault features enhances the performance of the machine learning algorithms. This study further illustrates that even though some machine learning algorithms provide similar high classification accuracy, they might differ in quantifying error Types I, II, and, III, which is extremely important for maintenance engineers, as it might lead to undetected fault events and false alarm events.

Keywords: predictive maintenance; machine learning; diagnostic analytics; erosion fault; crack fault; wind turbine blade (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: 2024
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