Professor Yiannis Papadopoulos Y.I.Papadopoulos@hull.ac.uk
Professor
Professor Yiannis Papadopoulos Y.I.Papadopoulos@hull.ac.uk
Professor
Costs related to maintenance of wind farm assets, including wind turbines, subsea infrastructure and grid, can be significantly reduced through condition monitoring, data analytics and advanced health management that addresses early fault diagnosis, prognosis and better informed maintenance planning. This can be achieved by a focused research programme that achieves a state-of-the-art synthesis of condition monitoring with reliability-centred maintenance and bio-inspired optimisation algorithms and tools pioneered by Hull.
Status | Project Complete |
---|---|
Value | £30,000.00 |
Project Dates | Sep 1, 2018 - Aug 31, 2021 |
Partner Organisations | No Partners |
H2020 - ICT - DEIS Feb 1, 2017 - Jan 31, 2020
Cyber-Physical-Systems harbor the potential for vast economic and societal impact in all major application domains, however in case of failure this may lead to catastrophic results for industry and society. Thus, ensuring the dependability of such sy...
Read More about H2020 - ICT - DEIS.
Secure and Safe Multi-Robot Systems Jan 1, 2021 - Dec 31, 2023
European strategy and research roadmap documents emphasise the significant societal and economic benefits coming from robotic and autonomous systems. Multi-Robot Systems (MRS) comprise distributed and interconnected robotic teams that can carry out t...
Read More about Secure and Safe Multi-Robot Systems.
The Alan Turing Institute - Post-Doctoral Enrichment Awards Mar 1, 2022 - Oct 31, 2022
Digital Twin through Physics-Informed Deep Learning for Offshore Wind Turbine Gearing Fault Diagnosis and Prognosis Mar 1, 2024 - Feb 28, 2025
Digital Twin (DT) technology combines real-time monitoring, predictive capabilities, and communication technologies to provide intelligent service systems capable of improving reliability, cost-effectiveness, and safety. DT research has seen a dramat...
Read More about Digital Twin through Physics-Informed Deep Learning for Offshore Wind Turbine Gearing Fault Diagnosis and Prognosis.
About Repository@Hull
Administrator e-mail: repository@hull.ac.uk
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Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
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