Predictive maintenance for offshore wind turbines through deep learning and online clustering of unsupervised subsystems: a real-world implementation

Abstract

<title>Abstract</title> <p>Enterprises in increasing numbers allocate substantial expenses to offshore wind energy development as a pivotal component of the global energy transition from fossil fuels, hence the importance of ensuring the reliability of offshore wind technology becomes ever more significant. At the same time, operation and maintenance (O&amp;M) of offshore wind farms are progressively focusing on the integration of artificial intelligence (AI) for enhancing the efficiency and performance of the wind energy facilities. Decision support strategies based on failure predictions are an important element in this trend. As a result, AI is more frequently used to create time-to-failure predictions based on large amount of data collected from sensors deployed to wind turbines. Nevertheless, unsupervised components or subsystems may occasionally lead to failures. This paper presents a real-life example that failures in unsupervised components can be reliably predicted by the use of AI. Two different methods, Support Vector Machine and Long Short Term Memory, are presented and their limitations and advantages discussed.</p>