Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.814010
Title: Machine learning techniques for the health monitoring of rotating machinery in nuclear power plants
Author: Costello, Jason J. A.
ISNI:       0000 0004 9353 0384
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2019
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Abstract:
This thesis explores the development of data-driven and machine learning methods in application to the health monitoring of rotating plant items being used in the primary and secondary cycles of the Advanced Gas-cooled Reactor (AGR) nuclear power plants in the UK. The methods fall broadly into two categories: the statistical augmentation of a pre-existing knowledge-based system for turbine generator vibration alarm analysis, and the development of a machine learning model for the exploration of long-term predictive measures of asset health for AGR gas circulator units. Both of these topics are unified in their engineering context, and the overall aim of the approaches employed: to provide improved decision support using data to reliability staff tasked with monitoring key nuclear assets. A self-tuning methodology for knowledge-based system parameterisation and data selection in rotomachinery vibration monitoring is introduced, providing a comparative study of numerous methods and case studies for features of interest in both steady-state and step change conditions. These approaches were developed using a historical dataset taken from a turbine generator in use at an AGR, with time series streams from multiple component channels. An event-driven approach to asset health is presented, utilising a support vector machine & logistic regression hybrid model to estimate particular states of interest associated with the gas circulator duty cycle. This approach to health monitoring (examining responses during semi-regular refuelling events) is shown to correlate highly with the remaining useful life of a circulator unit which eventually underwent an unexpected failure, and provides a potential quantitative metric for preventing repeat instances.
Supervisor: McArthur, Stephen ; West, Graeme Sponsor: Not available
Qualification Name: Thesis (Eng.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.814010  DOI:
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