With nuclear power plants accounting for 21% of the UK’s electrical generation, nuclear submarines underpinning national security, and unprecedented government economic investment programmes in infrastructure, nuclear engineering has returned to the forefront of the UK industrial attention. Lifetime extension programmes for existing civil and military assets necessitate improved monitoring, as well as modelling and prediction of equipment degradation throughout its lifetime.
As part of a £4.4m EPRSC Prosperity Partnership funded project led by the University of Strathclyde and engineering firm Babcock, the Lloyds Register Foundation programme on data-centric engineering at the Alan Turing Institute will lead the development of data-driven decision support systems. These aim to providing asset operators and managers access to enhanced information to enable faster, more effective and risk-informed decision making for safety critical operations.
Leveraging recent advances in machine learning and data science, these systems aim to integrate heterogeneous data streams from monitoring equipment, historical archives, and operator expertise in a semi-supervised fashion. Through recent developments in uncertainty quantification for deep models, we will develop a stable and realisable support framework. This will encompass both diagnostic applications, i.e. identification of faults and characterisation of severity, and prognostic applications, where one is interested in predicting the evolution of future equipment health.
The project also involves EDF Energy, Kinectrics Inc, Bruce Power, The Weir Group, BAM Nuttall, the University of Surrey, Cranfield University, and Imperial College.
We are recruiting a postdoctoral research assistant to work on this project. Please contact the organiser for more information or to express interest.