Development and demonstration of methods and tools for large scale wind turbine pitch bearing condition assessment (DemoBearing)

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The UK is No. 1 in the world for installed offshore wind power and continues the deployment in a predominant speed in the next few decades to meet 2050 carbon emissions targets. The increasing sizes of offshore wind turbines pose significant challenges in the operation and maintenance of all its components. In particular, wind turbine pitch bearing, as the safety-critical interface between the turbine blade and the hub to rotate the blade for power generation optimisation and emergency stop, is typified as the large, slow, partially rotated bearing but it is the weak part and bottleneck for large offshore turbines (Emerging grand challenge). In addition, the UK will have a large number of onshore turbines approaching the end of their design life by 2030. The pitch bearing poses a significant risk for the decision making in ageing turbine decommissioning or life extension (Upcoming challenge). In-situ pitch bearings condition assessment is a major and open challenge for the whole wind industry as there are no industrial standards available yet and few existing in-situ methods, such as endoscopy and grease analysis, can only partially assess the pitch bearing conditions. Therefore, it is essential to develop effective in-situ condition assessment methods and tools in order to reduce high maintenance cost, unplanned downtime and risk of catastrophic failure, improve reliability and energy efficiency of onshore and offshore wind power generation and enable reliable decision making in ageing onshore wind turbine life extension.The ambitious research is, for the first time and at the international forefront, to develop intelligent pitch bearing condition assessment methods and in-situ tools using vibration and acoustic emission measurements. In particular, the research tackles the global grand challenges in wind industry by addressing the fundamentally technical challenges related to weak, noisy, and non-stationary data analysis for large slow speed bearings. This will be achieved by developing novel algorithms with sparse signal separation, data fusion and machine learning methods, followed by significant demonstration activities on both lab and real world operating environments. The PI has developed the first industrial-scale wind turbine pitch bearing platform including three naturally damaged bearings with over 15 years operating life in a real wind farm and advanced data collection instrument. The newly built platform lays a solid foundation for the proposed research and creates an ideal platform for carrying out demonstration and impact activities. The PI has also secured the unique opportunity to carry out field data collection and demonstration in real world operating wind farms under the strongest supports provided by two industrial project partners.The data collected from three naturally damaged bearings will be made publicly available under open-source licences to enable other researchers to carry out condition assessment for large slow speed bearings. The IP developed during the project will be protected. The developed algorithms will be made publicly available, if not conflicted with the IP.The successful outcome of this project will break new ground in in-situ pitch bearing condition assessment methods and tools, contribute to industrial standards of pitch bearings, and benefit a wide range of industries that use large slow speed bearings, such as offshore oil, gas, mining and steel making, over many decades of bearing service life. The novel methods with regard to weak, noisy and non-stationary data analysis can be used for wide data-driven applications. Therefore, the project has a significant, wide and long term impact in the next few decades.