Supporting Analytics for Nuclear Asset Data Lifecycle

This PhD research will develop trustworthy analytical tools for the nuclear industry, extracting reliable information from plant condition monitoring (CM) data. Prognostics and Health Management (PHM) for critical assets operating in strictly regulated environments is a data intensive and time consuming task. The nuclear industry aims to improve upon the accuracy and efficiency of existing manual PHM approaches, through fundamental research into: new knowledge and data-driven AI techniques/processes, and the means of integration offering multiple sources of information in hybrid systems (stochastic and physics-based digital twin models). This PhD research will ask if innovative, disruptive technologies can be investigated and applied to real assets' CM data, to predict asset degradation and automatically assess the integrity of the PHM digital supply chain. The research will focus on the creation and translation of advances in analytical techniques and human-machine interfaces for industrial applications, so that nuclear operators can confidently interact with the wealth of data available from legacy and future assets.

The technical challenge requires dedicated PhD depth investigation to cut across previously exclusive disciplines that require a longer time to assimilate novel contributions, e.g.: encoding domain expert knowledge; physics-based model development; or, machine learning of unobservable relations (virtual metrology or surrogate sensors). Novelty is in identifying candidate models to validate representative case studies, e.g.: physics models with large numbers of variables where relevant inputs are unclear; complex relations in digital twinning; encoding uncertainty into physics-based models.

The PhD will form part of the Advanced Nuclear Research Centre's (ANRC) PhD portfolio at the University of Strathclyde, who are an NPL strategic partner, and will bring together stakeholders in the nuclear supply chain to better integrate their needs and requirements.