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

4f806b0a-7a8e-426c-abb7-3ab932374726

Closed

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

£845,615

May 28, 2019

Dec. 31, 2021

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.

Potential Impact:
Demobearing focuses on developing in-situ pitch bearing condition assessment methods and tools by addressing one key and fundamental issue in emerging offshore and ageing onshore wind industry in order to reduce high maintenance cost, risk of catastrophic failure and improve the reliability of wind power generation. The outcome also benefits other industries that use large slow speed bearings.
(1) The two project partners, Acciona (a worldwide large wind operator) and EnergieKontor (a European leading wind operator managing a number of wind farms in the UK) will be the direct beneficiaries of the output of this research. The novel methods and tools for pitch bearing condition assessment will enable the two partners to minimise the risk of turbine failure, and to reduce the maintenance cost and to provide reliable and low cost wind power for the end users. The main route to impact is the planned laboratory and field demonstration through the partners' strong supports.
(2) In addition to the two direct beneficiaries, the wider wind farm operators (over 2000 operators worldwide) and numerous maintenance providers (including bearing manufacturers who also provide maintenance service) could benefit from the research and the developed methods and tools. The PI will enhance his existing contact with several wind operators and create new industrial links by actively engaging the ongoing projects (such as Offshore-HOME) within the School, the EPSRC Centres for Doctoral Training in Power Networks based at the University of Manchester, and the Supergen wind or ORE (offshore renewable energy) Consortium which has a number of large wind operators and service providers.
(3) The impact of the research is well beyond wind industry. The outcomes will inform and engage the general slow speed bearing industry and end-users in other sectors such as the oil, gas, steel, maritime and military industry. The impact activities on wide sectors will mainly focus on exhibition on large conferences and events via the supports of the Faculty business engagement support team (BEST). The BEST has exhibiting stands on a number of large conferences and events each year to reach hundreds of thousands of audiences. The PI has experience in working with BEST to exhibit this research at Innovate 2017.
(4) The International Electrotechnical Commission (IEC) and International Organization for Standardization (ISO) are the professional bodies who set standards for wind turbines and bearings. As existing standards (such as ISO16079-1 and IEC 61400-25) do not cover the pitch bearing, the successful outcome of this research will be provided to the British Standards Institution through its online feedback collection system in order to add new guides or justify future revisions to guides of pitch bearing condition assessment.
(5) A unique impact from this research is the publicly available pitch bearing data. The data collected from naturally damaged bearings will be made publicly available through the university open data repository. The IP developed during the project will be protected but to ensure the wide usage of the algorithms under open-source licences. All the created algorithms will be made publicly available through Github (online open-source code hosting platform) if not conflicted with the IP.
(6) High profile and high impact publications (such as IEEE Transactions on Industrial Electronics) and conferences (such as European Wind, previously EWEA) with open access will ensure maximum readership of key results from the work.
(7) To ensure wider public awareness of the research and bring the research to life, the PI and his team will engage with University Media Service team to make a video case study that will summarise the results and achievements. Further, the unique industrial-scale pitch bearing platform will be used for the School open days and outreach activities to inspire next generation.