This is the final version. Available from IOP Publishing via the DOI in this record.Gearboxes are a complex, yet vital assembly for non-direct-drive offshore wind turbines, which are designed to last for the lifetime of the asset. However, recent studies indicate that they may have to be replaced as early as 6.5 years. Moreover, their contribution to offshore wind farm failures and downtime has been shown to be amongst the three most critical assemblies with the highest material cost required. An improved understanding of these premature failures and the ability to predict them in advance could reduce inspection and maintenance costs, as well as to help overcome many logistical and planning challenges. The objective of this paper is to present the lessons learnt from a gearbox exchange performed in one of the offshore wind turbines at Teesside offshore wind farm, comprising 27 2.3MW wind turbines. The paper takes a condition monitoring perspective and uses the identified spalling at the inner part of the planetary bearing as the governing failure mode. A data management system has been setup, incorporating all the operational data received, including maintenance log information and sensor data. A period of up to 2.5 years, prior to the the gearbox exchange, is examined for this study. SCADA and CMS data of the faulty turbine are compared against the wind farm, using statistical methods and machine learning techniques. Supervised learning models are built, which will help predict similar failures in the future. Results show how different data sources can contribute in gearbox failure diagnosis and help to expedite failure detection for Teesside offshore wind farm and similar wind turbine and gearbox types. This paper will be of interest to wind farm developers and operators to build predictive models from monitoring data that can forecast potential gearbox failures.Energy Technology InstituteResearch Council Energy ProgrammeEuropean Unions Horizon 202
