Predictive maintenance for offshore wind turbines through deep learning and online clustering of unsupervised subsystems: a real-world implementation
Abstract
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&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 demonstrates a practical application of AI for predicting failures in unsupervised components. Specifically, we focus on a single component: the yaw brakes of a 3 MW wind turbine. The study analyses how the brake pads of these yaw brakes wear out over time, using the data collected from turbine controllers. To predict when these failures are likely to occur, we employ Long-Short-Term Memory (LSTM) which is empowered by a pre-processed dataset using Support Vector Machine (SVM) for clustering of the relevant data. This combination of SVM and LSTM presents an alternative approach to enhancing predictive maintenance strategies, which can improve the operational reliability and cost-efficiency of offshore wind energy systems.