Condition Monitoring of Wind Turbine Drivetrain Bearings

Abstract

Abstract The current pace of renewable energy development around the world is unprecedented, with offshore wind in particular proving to be an extremely valuable and reliable energy source. The global installed capacity of offshore wind turbines by the end of 2022 is expected to reach the 46.4 GW, among which 33.9 GW in Europe. Costs are critical for the future success of the offshore wind sector. The industry is pushing hard to make cost reductions to show that offshore wind is economically comparable to conventional fossil fuels. Efficiencies in Operations and Maintenance offer potential to achieve significant cost savings as it accounts for around 20% - 30% of overall offshore wind farm costs. One of the most critical assembly of onshore, offshore and floating wind turbines is the gearbox. Therefore sensing and condition monitoring systems for wind turbines are needed in order to obtain reliable information on the state and condition of different critical parts, focusing towards the detection and/or prediction of damage before it reaches a critical stage. The aim of this paper is the application and evaluation of advanced diagnostic techniques and diagnostic indicators, including the Enhanced Envelope Spectrum and the Spectral Flatness on real world vibration data collected from vibration sensors on gearboxes in multiple wind turbines over an extended period of time of nearly four years. The diagnostic indicators are compared with classical statistic indicators, i.e. Kurtosis, Crest Factor etc. and their effectiveness is evaluated based on the successful detection of two failure events.