{"id":{"repo_id":"strathclyde","oai_identifier":"oai:strathclyde:1j92g746t"},"canonical_url":"https://search.dev.ndltd.org/etd/strathclyde/oai:strathclyde:1j92g746t","repository":{"repo_id":"strathclyde","name":"University of Strathclyde","base_url":"https://stax.strath.ac.uk/catalog/oai"},"display":{"title":"Machine learning techniques for the health monitoring of rotating machinery in nuclear power plants","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Costello, Jason J. 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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."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Machine learning techniques for the health monitoring of rotating machinery in nuclear power plants"]}]}],"canonical_facts":{"dc:contributor.advisor":["West, Graeme","McArthur, Stephen, 1971-"],"dc:creator":["Costello, Jason J. 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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."],"dc:description.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."],"dc:identifier":["T15480"],"dc:identifier.doi":["10.48730/cyzq-6d67"],"dc:identifier.uri":["https://stax.strath.ac.uk/concern/theses/1j92g746t"],"dc:publisher.department":["Institute of Energy and Environment"],"dc:publisher.institution":["University of Strathclyde"],"dc:title":["Machine learning techniques for the health monitoring of rotating machinery in nuclear power plants"],"dc:type.qualificationlevel":["doctoral-pg"],"dc:type.qualificationname":["engd"]},"updated_at":"2026-07-24T04:41:57Z"}