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Open Access research with a European policy impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the European Policies Research Centre (EPRC).

EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

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Prognostic modelling utilizing a high fidelity pressurized water reactor simulator

McGhee, Mark J. and Catterson, Victoria M. and Brown, Blair (2017) Prognostic modelling utilizing a high fidelity pressurized water reactor simulator. IEEE Transactions on Systems Man and Cybernetics: Systems. ISSN 2168-2216

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Within power generation, aging assets and an emphasis on more efficient operation of power systems and improved maintenance decision methods has led to a growing focus on asset prognostics. The main challenge facing the implementation of successful asset prognostics in power generation is the lack of available run-to-failure data. This paper proposes to overcome this issue by use of full-scope high-fidelity simulators to generate the run-to-failure data required. From this simulated failure data a similarity-based prognostic approach is developed for estimating the remaining useful life of a valve asset. Case study data is generated by initializing prebuilt industrial failure models within a 970 MW pressurized water reactor simulation. Such full-scope high-fidelity simulators are mainly operated for training purposes, allowing personnel to gain experience of standard operation as well as failures within a safe, simulated operating environment. This paper repurposes such a high-fidelity simulator to generate the type of data and affects that would be produced in the event of a fault. The fault scenario is then run multiple times to generate a library of failure events. This library of events was then split into training and test batches for building the prognostic model. Results are presented and conclusions drawn about the success of the technique and the use of high-fidelity simulators in this manner.