{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/41296"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/41296","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Industrial Scalable Rolling Element Bearing Diagnostic and Prognostic Modelling","abstract":"Rolling element bearings are a critical component in nearly any rotating system. They operate at significant loads and speeds and must withstand various forms of harsh environmental factors. Due to this, they can be prone to rolling contact fatigue failure, especially in industrial applications such as wind turbines and both commercial and military aircraft. The following thesis extends published diagnostic models for bearing condition through inline wear debris sensors through experimental observations and a physical understanding of the bearing degradation mechanics. This diagnostic classification model is scalable to bearings of other sizes as its predecessors, with considerations for differently sized inline wear debris sensors. Furthermore, this diagnostic model is then combined with particle filters and mathematical representations of the bearing degradation curve to estimate the remaining useful life of the bearing, with consideration for both the bearing load and speed.","abstract_html":"Rolling element bearings are a critical component in nearly any rotating system. They operate at significant loads and speeds and must withstand various forms of harsh environmental factors. Due to this, they can be prone to rolling contact fatigue failure, especially in industrial applications such as wind turbines and both commercial and military aircraft. The following thesis extends published diagnostic models for bearing condition through inline wear debris sensors through experimental observations and a physical understanding of the bearing degradation mechanics. This diagnostic classification model is scalable to bearings of other sizes as its predecessors, with considerations for differently sized inline wear debris sensors. Furthermore, this diagnostic model is then combined with particle filters and mathematical representations of the bearing degradation curve to estimate the remaining useful life of the bearing, with consideration for both the bearing load and speed.","abstract_has_math":false,"creators":["Mahmoud, Hassan"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Mechanical","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T01:34:24Z","subjects":[],"languages":["en"],"rights":["Copyright © 2023 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.22215/etd/2023-16010"],"render_values":[{"text":"10.22215/etd/2023-16010","href":"https://doi.org/10.22215/etd/2023-16010","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14718/41296","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Mahmoud, Hassan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-08T20:16:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-08T20:16:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"dc:publisher","label":"Institution","values":["Carleton University"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering, Mechanical"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (M.App.Sc.)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright © 2023 the author(s). 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They operate at significant loads and speeds and must withstand various forms of harsh environmental factors. Due to this, they can be prone to rolling contact fatigue failure, especially in industrial applications such as wind turbines and both commercial and military aircraft. The following thesis extends published diagnostic models for bearing condition through inline wear debris sensors through experimental observations and a physical understanding of the bearing degradation mechanics. This diagnostic classification model is scalable to bearings of other sizes as its predecessors, with considerations for differently sized inline wear debris sensors. Furthermore, this diagnostic model is then combined with particle filters and mathematical representations of the bearing degradation curve to estimate the remaining useful life of the bearing, with consideration for both the bearing load and speed."]},{"key":"dc:title","label":"Title","values":["Industrial Scalable Rolling Element Bearing Diagnostic and Prognostic Modelling"]}]}],"canonical_facts":{"dc:creator":["Mahmoud, Hassan"],"dc:date.accessioned":["2025-04-08T20:16:26Z"],"dc:date.available":["2025-04-08T20:16:26Z"],"dc:date.issued":["2023"],"dc:description.abstract":["Rolling element bearings are a critical component in nearly any rotating system. They operate at significant loads and speeds and must withstand various forms of harsh environmental factors. Due to this, they can be prone to rolling contact fatigue failure, especially in industrial applications such as wind turbines and both commercial and military aircraft. The following thesis extends published diagnostic models for bearing condition through inline wear debris sensors through experimental observations and a physical understanding of the bearing degradation mechanics. This diagnostic classification model is scalable to bearings of other sizes as its predecessors, with considerations for differently sized inline wear debris sensors. Furthermore, this diagnostic model is then combined with particle filters and mathematical representations of the bearing degradation curve to estimate the remaining useful life of the bearing, with consideration for both the bearing load and speed."],"dc:identifier.doi":["10.22215/etd/2023-16010"],"dc:identifier.uri":["https://hdl.handle.net/20.500.14718/41296"],"dc:language.iso":["en"],"dc:publisher":["Carleton University"],"dc:rights":["Copyright © 2023 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. 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