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Digital Twin through Physics-Informed Deep Learning for Offshore Wind Turbine Gearing Fault Diagnosis and Prognosis

People Involved

Mr Eamonn Tuton

Project Description

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 dramatic rise in recent years, notably in offshore wind energy where long distances from shore and harsh weather conditions increase Operations and Maintenance (O&M) costs and difficulty. Much of the current DT research focuses on the prediction of individualised component failures without consideration of the wider systems necessary for real-world deployment, such as the combination of DT-based predictions with maintenance planning. Additionally, whilst research focuses on maximising reliability and cost effectiveness, little consideration has been given to the environmental and health and safety aspects associated with these. The proposed project therefore proposes to develop a deployable DT service system comprisinges three main parts; a physical wind turbine, a DT model of said turbine, and a maintenance system. Sensors, embedded within key turbine components, such as in bearings, gears, and the general structure, provide operational data to the DT model in real-time. This data is then used by physics-informed machine learning models to provide live predictions of future component degradation. This allows for predictions of potential failure, remaining useful life, and general health of monitored components. The maintenance system then assesses this information to make informed decisions for the planning of O&M activities, taking environmental and health and safety aspects into consideration. Finally, monitoring, prediction, and planning data are stored to inform and improve future system operation.

Status Project Live
Funder(s) University of Hull
Value £5,000.00
Project Dates Mar 1, 2024 - Feb 28, 2025
Partner Organisations Indian Institute Of Technology - Madras


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