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dc.contributor.authorPark, Sangkyun
dc.contributor.authorKim, Juhyung
dc.contributor.authorKim, Jaejun
dc.contributor.authorWang, Seunghyeon
dc.date.accessioned2026-08-20T21:24:57Z
dc.date.available2026-08-20T21:24:57Z
dc.date.copyright2025/06/13
dc.date.issued2025
dc.identifier.otherJCCEE5.CPENG-6677.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314415
dc.description.abstractAbstractFault detection and diagnosis (FDD) in air handling units (AHUs) is essential for ensuring indoor air quality and prolonging system life span. However, the use of real operational data for FDD has been limited in existing research, largely due to ...Practical ApplicationsThis study demonstrates how nine different machine-learning methods—ranging from traditional approaches like naïve Bayes (NB) and decision trees (DT) to advanced models like artificial neural networks (ANN) and Transformer-based ...
dc.publisherAmerican Society of Civil Engineers
dc.titleFault Diagnosis of Air Handling Units in an Auditorium Using Real Operational Labeled Data across Different Operation Modes
dc.typeJournal Article
dc.journal.volume39
dc.journal.issue5
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-6677
dc.journal.fristpage04025065-1
dc.journal.lastpage04025065-18
dc.page18
dc.treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 005
dc.contenttypeFulltext


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