{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1415"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1415","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Predictive analytics for maintenance activities in nuclear power plants: a feasibility study","abstract":"Nuclear power plants are known for their use of legacy systems and processes. As plants age, the amount of maintenance increases while resources remain finite, leading to unwanted delays, affecting the health of assets and increasing costs. To aid in the modernization and digitization of nuclear power plants, this work explores data driven methods, including statistical and machine learning techniques to predict target variables. Representative Naval Propulsion Plant data with variables similar to that in the nuclear industry are used as nuclear data is not available in the public domain. Experimental results confirm target variables can be predicted with relatively high accuracy, with Deep Learning methods harbouring the lowest relative error. Two frameworks are developed based on results to showcase how predictive analytics can be used in nuclear power plant maintenance. This work is a proof of concept informing stakeholders that data driven approaches are viable in reducing maintenance delays.","abstract_html":"Nuclear power plants are known for their use of legacy systems and processes. As plants age, the amount of maintenance increases while resources remain finite, leading to unwanted delays, affecting the health of assets and increasing costs. To aid in the modernization and digitization of nuclear power plants, this work explores data driven methods, including statistical and machine learning techniques to predict target variables. Representative Naval Propulsion Plant data with variables similar to that in the nuclear industry are used as nuclear data is not available in the public domain. Experimental results confirm target variables can be predicted with relatively high accuracy, with Deep Learning methods harbouring the lowest relative error. Two frameworks are developed based on results to showcase how predictive analytics can be used in nuclear power plant maintenance. This work is a proof of concept informing stakeholders that data driven approaches are viable in reducing maintenance delays.","abstract_has_math":false,"creators":["Khurmi, Rajinder"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Sankaranarayanan, Karthik","Harvel, Glenn"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-12-01","date_published":"2021-12-01","updated_at":"2026-07-24T05:35:41Z","subjects":["Nuclear power plants","Machine learning","Predictive analytics","Digitization and modernization","Engineering management"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1415","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sankaranarayanan, Karthik","Harvel, Glenn"]},{"key":"dc:creator","label":"Author","values":["Khurmi, Rajinder"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-02-07T21:25:52Z","2022-03-29T17:27:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-02-07T21:25:52Z","2022-03-29T17:27:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Nuclear power plants","Machine learning","Predictive analytics","Digitization and modernization","Engineering management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1415"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Nuclear power plants are known for their use of legacy systems and processes. As plants age, the amount of maintenance increases while resources remain finite, leading to unwanted delays, affecting the health of assets and increasing costs. To aid in the modernization and digitization of nuclear power plants, this work explores data driven methods, including statistical and machine learning techniques to predict target variables. Representative Naval Propulsion Plant data with variables similar to that in the nuclear industry are used as nuclear data is not available in the public domain. Experimental results confirm target variables can be predicted with relatively high accuracy, with Deep Learning methods harbouring the lowest relative error. Two frameworks are developed based on results to showcase how predictive analytics can be used in nuclear power plant maintenance. This work is a proof of concept informing stakeholders that data driven approaches are viable in reducing maintenance delays."]},{"key":"dc:title","label":"Title","values":["Predictive analytics for maintenance activities in nuclear power plants: a feasibility study"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sankaranarayanan, Karthik","Harvel, Glenn"],"dc:creator":["Khurmi, Rajinder"],"dc:date.accessioned":["2022-02-07T21:25:52Z","2022-03-29T17:27:18Z"],"dc:date.available":["2022-02-07T21:25:52Z","2022-03-29T17:27:18Z"],"dc:date.issued":["2021-12-01"],"dc:description.abstract":["Nuclear power plants are known for their use of legacy systems and processes. As plants age, the amount of maintenance increases while resources remain finite, leading to unwanted delays, affecting the health of assets and increasing costs. To aid in the modernization and digitization of nuclear power plants, this work explores data driven methods, including statistical and machine learning techniques to predict target variables. Representative Naval Propulsion Plant data with variables similar to that in the nuclear industry are used as nuclear data is not available in the public domain. Experimental results confirm target variables can be predicted with relatively high accuracy, with Deep Learning methods harbouring the lowest relative error. Two frameworks are developed based on results to showcase how predictive analytics can be used in nuclear power plant maintenance. This work is a proof of concept informing stakeholders that data driven approaches are viable in reducing maintenance delays."],"dc:identifier.uri":["https://hdl.handle.net/10155/1415"],"dc:language.iso":["en"],"dc:subject":["Nuclear power plants","Machine learning","Predictive analytics","Digitization and modernization","Engineering management"],"dc:title":["Predictive analytics for maintenance activities in nuclear power plants: a feasibility study"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:41Z"}