{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125628"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125628","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enhancing cleanroom operations: From preventive equipment maintenance to lethal chemical asset tracking","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Yaohui (Cody) Wang, accepted the attached license on 2024-07-12 at 12:15.","The student, Yaohui (Cody) Wang, submitted this Thesis for approval on 2024-07-12 at 12:24.","This Thesis was approved for publication on 2024-07-17 at 11:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21093 on 2025-02-04 at 21:05:15","This thesis introduces a comprehensive Internet of Things(IoT) monitoring framework tailored for academic cleanrooms, focusing on innovative solutions for preventive equipment maintenance and hazardous chemical tracking. The proposed framework is elucidated through two case studies: EcoMAT and MachineStethoscope. The first case study, EcoMAT, presents a novel asset tracking system employing permanent magnets to monitor hazardous chemicals. This magnet-based approach provides a cost-effective, highly accurate alternative to conventional Computer Vision(CV) and Radio Frequency(RF) systems. The system’s evaluation in cleanroom settings demonstrates its high localization precision, low latency, and minimal power consumption, highlighting its suitability for environments that demand stringent contamination control. The second case study, MachineStethoscope, details the development of a portable, edge- based Machine Health Monitoring System(MHMS). Leveraging IoT sensors and advanced data analytics, this system offers real-time, on-device anomaly detection. Field tests in academic laboratories affirm its effectiveness in detecting machine anomalies early, thereby reducing downtime and maintenance costs. Together, these case studies exemplify the application of the proposed IoT monitoring framework, providing scalable and practical solutions to enhance operational efficiency and safety in academic cleanrooms. This research significantly contributes to the optimization of maintenance practices and chemical tracking, ensuring the integrity and sustainability of scientific research environments."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enhancing cleanroom operations: From preventive equipment maintenance to lethal chemical asset tracking"]}]}],"canonical_facts":{"dc:contributor":["Nahrstedt, Klara"],"dc:creator":["Wang, Yaohui (Cody)"],"dc:date":["2024-07-17","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Yaohui (Cody) Wang, accepted the attached license on 2024-07-12 at 12:15.","The student, Yaohui (Cody) Wang, submitted this Thesis for approval on 2024-07-12 at 12:24.","This Thesis was approved for publication on 2024-07-17 at 11:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21093 on 2025-02-04 at 21:05:15","This thesis introduces a comprehensive Internet of Things(IoT) monitoring framework tailored for academic cleanrooms, focusing on innovative solutions for preventive equipment maintenance and hazardous chemical tracking. The proposed framework is elucidated through two case studies: EcoMAT and MachineStethoscope. The first case study, EcoMAT, presents a novel asset tracking system employing permanent magnets to monitor hazardous chemicals. This magnet-based approach provides a cost-effective, highly accurate alternative to conventional Computer Vision(CV) and Radio Frequency(RF) systems. The system’s evaluation in cleanroom settings demonstrates its high localization precision, low latency, and minimal power consumption, highlighting its suitability for environments that demand stringent contamination control. The second case study, MachineStethoscope, details the development of a portable, edge- based Machine Health Monitoring System(MHMS). Leveraging IoT sensors and advanced data analytics, this system offers real-time, on-device anomaly detection. Field tests in academic laboratories affirm its effectiveness in detecting machine anomalies early, thereby reducing downtime and maintenance costs. Together, these case studies exemplify the application of the proposed IoT monitoring framework, providing scalable and practical solutions to enhance operational efficiency and safety in academic cleanrooms. This research significantly contributes to the optimization of maintenance practices and chemical tracking, ensuring the integrity and sustainability of scientific research environments."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125628"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Yaohui (Cody) Wang"],"dc:subject":["Internet Of Things(iot)","Asset Tracking","Cyber Physical Systems","Embedded Systems","Cleanrooms"],"dc:title":["Enhancing cleanroom operations: From preventive equipment maintenance to lethal chemical asset tracking"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}