Offshore wind assets have reached multi-GW scale and additional capacity is being installed and developed. To achieve demanding cost of energy targets, awarded by competitive auctions, the operation and maintenance (O&M) of these assets has to become increasingly efficient, whilst ensuring compliance and effectiveness. Existing offshore wind farm assets generate a significant amount of inhomogeneous data related to O&M processes. These data contain rich information about the condition of the assets, which is rarely fully utilized by the operators and service providers. Academic and industrial research and development efforts have led to a suite of tools trying to apply sensor data and build machine learning models to diagnose, trend and predict component failures. This study presents a decision support framework incorporating a range of different supervised and un-supervised learning algorithms. The aim is to provide guidance for asset owners on how to select the most relevant datasets, apply and choose the different machine learning algorithms and how to integrate the data stream with daily maintenance procedures. The presented methodology is tested on a real case example of an offshore wind turbine gearbox replacement at Teesside offshore wind farm. The study uses k-nearest neighbour (kNN) and support vector machine (SVM) algorithms to detect the fault using supervisory control and data acquisition (SCADA) data and an autoregressive model for the vibration data of the condition monitoring system (CMS). The implementation of all the algorithms has resulted in an accuracy higher than 94%. The results of this paper will be of interest to offshore wind farm developers and operators to streamline and optimize their O&M planning activities for their assets and reduce the associated costs.

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