{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/317980"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/317980","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"ENHANCING BUILDING FAULT DETECTION, DIAGNOSTICS, AND PROGNOSTICS THROUGH A HYBRID PHYSICS-INFORMED MODELING FRAMEWORK FOR OVERCOMING QUANTITY AND TEMPORAL DATA SCARCITY","abstract":"Effective Fault Detection, Diagnosis, and Prognosis (FDDP) in buildings is severely hindered by the scarcity of real-world fault data. This thesis proposes a hybrid physics-informed modeling framework to overcome both data quantity scarcity and temporal sparsity. First, Hybrid Conditional Generative Adversarial Network (HCGAN) is developed. By leveraging physics-based simulations as conditional priors, it generates high-fidelity synthetic fault data, enabling zero-shot diagnosis without requiring real fault samples. Second, to address temporal sparsity in prognosis, Dual-Calibrated Particle Filter (DC-PF) is proposed. It fuses uncalibrated surrogate models with sparse observations to accurately estimate Remaining Useful Life (RUL). Experimental results demonstrate significant performance gains: HCGAN improves data fidelity by 50% over baselines, while DC-PF reduces prognosis error by roughly 57%. Integrated into a unified pipeline, this framework transforms sparse operational data into reliable, proactive maintenance decisions, providing a practical path for scalable smart building management.","abstract_html":"Effective Fault Detection, Diagnosis, and Prognosis (FDDP) in buildings is severely hindered by the scarcity of real-world fault data. This thesis proposes a hybrid physics-informed modeling framework to overcome both data quantity scarcity and temporal sparsity. First, Hybrid Conditional Generative Adversarial Network (HCGAN) is developed. By leveraging physics-based simulations as conditional priors, it generates high-fidelity synthetic fault data, enabling zero-shot diagnosis without requiring real fault samples. Second, to address temporal sparsity in prognosis, Dual-Calibrated Particle Filter (DC-PF) is proposed. It fuses uncalibrated surrogate models with sparse observations to accurately estimate Remaining Useful Life (RUL). Experimental results demonstrate significant performance gains: HCGAN improves data fidelity by 50% over baselines, while DC-PF reduces prognosis error by roughly 57%. Integrated into a unified pipeline, this framework transforms sparse operational data into reliable, proactive maintenance decisions, providing a practical path for scalable smart building management.","abstract_has_math":false,"creators":["HAN JINTONG"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-22","date_published":"2025-08-22","updated_at":"2026-07-24T03:31:13Z","subjects":["Building Automation","Remaining Useful Life","Generative Data Augmentation","Predictive Maintenance","Fault Prognosis","Fault Detection and Diagnosis"],"languages":[],"rights":[],"rights_urls":["https://scholarbank.nus.edu.sg/bitstreams/cfe3d8b3-2146-4634-be56-4d7b271796c1/download"],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["HAN JINTONG"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-08-22"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://scholarbank.nus.edu.sg/handle/10635/317980"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Building Automation","Remaining Useful Life","Generative Data Augmentation","Predictive Maintenance","Fault Prognosis","Fault Detection and Diagnosis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["https://scholarbank.nus.edu.sg/bitstreams/cfe3d8b3-2146-4634-be56-4d7b271796c1/download"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarbank.nus.edu.sg/bitstreams/5f047e61-e703-4cd5-b62a-5f169214fa4c/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Effective Fault Detection, Diagnosis, and Prognosis (FDDP) in buildings is severely hindered by the scarcity of real-world fault data. 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First, Hybrid Conditional Generative Adversarial Network (HCGAN) is developed. By leveraging physics-based simulations as conditional priors, it generates high-fidelity synthetic fault data, enabling zero-shot diagnosis without requiring real fault samples. Second, to address temporal sparsity in prognosis, Dual-Calibrated Particle Filter (DC-PF) is proposed. It fuses uncalibrated surrogate models with sparse observations to accurately estimate Remaining Useful Life (RUL). Experimental results demonstrate significant performance gains: HCGAN improves data fidelity by 50% over baselines, while DC-PF reduces prognosis error by roughly 57%. 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