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Driving innovations in manufacturing: Open Access research from DMEM

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Design, Manufacture & Engineering Management (DMEM).

Centred on the vision of 'Delivering Total Engineering', DMEM is a centre for excellence in the processes, systems and technologies needed to support and enable engineering from concept to remanufacture. From user-centred design to sustainable design, from manufacturing operations to remanufacturing, from advanced materials research to systems engineering.

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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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Abstract

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.