{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2097"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2097","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"A secure multi-layer embedded framework for real-time fault detection in Industrial IoT power supply systems","abstract":"Industrial power supplies are traditionally designed as passive components with limited diagnostic visibility and secure remote supervision. This thesis presents the design and experimental validation of a secure, multi-layer Industrial Internet of Things (IIoT)-enabled power-supply platform that integrates deterministic protection, embedded diagnostics, and authenticated remote monitoring within a unified architecture. The platform combines real-time hard-fault protection with quantized neural network (QNN)-based soft-fault diagnostics executed on a microcontroller-class device, enabling early detection of incipient degradation with bounded latency. Industrial interoperability is maintained through Modbus RTU communication, while secure access is provided via TLS-encrypted HTTPS and a web-based dashboard. Experimental results demonstrate stable electrical operation under sustained thermal stress, consistent communication timing with an average latency of 5.76 ms, and diagnostic performance exceeding 96%, with sub-millisecond inference latency. These results demonstrate that intelligence, connectivity, and cybersecurity can be co-designed while preserving bounded real-time behavior and operational reliability.","abstract_html":"Industrial power supplies are traditionally designed as passive components with limited diagnostic visibility and secure remote supervision. This thesis presents the design and experimental validation of a secure, multi-layer Industrial Internet of Things (IIoT)-enabled power-supply platform that integrates deterministic protection, embedded diagnostics, and authenticated remote monitoring within a unified architecture. The platform combines real-time hard-fault protection with quantized neural network (QNN)-based soft-fault diagnostics executed on a microcontroller-class device, enabling early detection of incipient degradation with bounded latency. Industrial interoperability is maintained through Modbus RTU communication, while secure access is provided via TLS-encrypted HTTPS and a web-based dashboard. Experimental results demonstrate stable electrical operation under sustained thermal stress, consistent communication timing with an average latency of 5.76 ms, and diagnostic performance exceeding 96%, with sub-millisecond inference latency. These results demonstrate that intelligence, connectivity, and cybersecurity can be co-designed while preserving bounded real-time behavior and operational reliability.","abstract_has_math":false,"creators":["Tran, Ba Vu"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Youssef, Mohamed"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-04-01","date_published":"2026-04-01","updated_at":"2026-07-24T05:35:20Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2097","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Youssef, Mohamed"]},{"key":"dc:creator","label":"Author","values":["Tran, Ba Vu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-04-28T20:05:42Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"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/10155/2097"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Industrial power supplies are traditionally designed as passive components with limited diagnostic visibility and secure remote supervision. This thesis presents the design and experimental validation of a secure, multi-layer Industrial Internet of Things (IIoT)-enabled power-supply platform that integrates deterministic protection, embedded diagnostics, and authenticated remote monitoring within a unified architecture. The platform combines real-time hard-fault protection with quantized neural network (QNN)-based soft-fault diagnostics executed on a microcontroller-class device, enabling early detection of incipient degradation with bounded latency. Industrial interoperability is maintained through Modbus RTU communication, while secure access is provided via TLS-encrypted HTTPS and a web-based dashboard. Experimental results demonstrate stable electrical operation under sustained thermal stress, consistent communication timing with an average latency of 5.76 ms, and diagnostic performance exceeding 96%, with sub-millisecond inference latency. These results demonstrate that intelligence, connectivity, and cybersecurity can be co-designed while preserving bounded real-time behavior and operational reliability."]},{"key":"dc:title","label":"Title","values":["A secure multi-layer embedded framework for real-time fault detection in Industrial IoT power supply systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Youssef, Mohamed"],"dc:creator":["Tran, Ba Vu"],"dc:date.accessioned":["2026-04-28T20:05:42Z"],"dc:date.issued":["2026-04-01"],"dc:description.abstract":["Industrial power supplies are traditionally designed as passive components with limited diagnostic visibility and secure remote supervision. This thesis presents the design and experimental validation of a secure, multi-layer Industrial Internet of Things (IIoT)-enabled power-supply platform that integrates deterministic protection, embedded diagnostics, and authenticated remote monitoring within a unified architecture. The platform combines real-time hard-fault protection with quantized neural network (QNN)-based soft-fault diagnostics executed on a microcontroller-class device, enabling early detection of incipient degradation with bounded latency. Industrial interoperability is maintained through Modbus RTU communication, while secure access is provided via TLS-encrypted HTTPS and a web-based dashboard. Experimental results demonstrate stable electrical operation under sustained thermal stress, consistent communication timing with an average latency of 5.76 ms, and diagnostic performance exceeding 96%, with sub-millisecond inference latency. These results demonstrate that intelligence, connectivity, and cybersecurity can be co-designed while preserving bounded real-time behavior and operational reliability."],"dc:identifier.uri":["https://hdl.handle.net/10155/2097"],"dc:language.iso":["en"],"dc:title":["A secure multi-layer embedded framework for real-time fault detection in Industrial IoT power supply systems"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:20Z"}