A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines

Maryna Garan, Khaoula Tidriri and Iaroslav Kovalenko
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Maryna Garan: Faculty of Mechanical Engineering, Department of Manufacturing Systems and Automation, Technical University of Liberec, 46117 Liberec, Czech Republic
Khaoula Tidriri: CNRS (Centre National de la Recherche Scientifique), Grenoble INP (Institut Polytechnique), GIPSA-Lab (Grenoble Images Parole Signal Automatique), Université Grenoble Alpes, 38000 Grenoble, France
Iaroslav Kovalenko: Faculty of Mechanical Engineering, Department of Manufacturing Systems and Automation, Technical University of Liberec, 46117 Liberec, Czech Republic

Energies, 2022, vol. 15, issue 3, 1-21

Abstract: Nowadays, the energy sector is experiencing a profound transition. Among all renewable energy sources, wind energy is the most developed technology across the world. To ensure the profitability of wind turbines, it is essential to develop predictive maintenance strategies that will optimize energy production while preventing unexpected downtimes. With the huge amount of data collected every day, machine learning is seen as a key enabling approach for predictive maintenance of wind turbines. However, most of the effort is put into the optimization of the model architectures and its parameters, whereas data-related aspects are often neglected. The goal of this paper is to contribute to a better understanding of wind turbines through a data-centric machine learning methodology. In particular, we focus on the optimization of data preprocessing and feature selection steps of the machine learning pipeline. The proposed methodology is used to detect failures affecting five components on a wind farm composed of five turbines. Despite the simplicity of the used machine learning model (a decision tree), the methodology outperformed model-centric approach by improving the prediction of the remaining useful life of the wind farm, making it more reliable and contributing to the global efforts towards tackling climate change.

Keywords: wind turbines; predictive maintenance; supervisory control and data acquisition; decision tree; feature importance; high correlation filter; mutual information; principal component analysis; independent component analysis; Energias de Portugal (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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