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contributor authorPark, Sangkyun
contributor authorKim, Juhyung
contributor authorKim, Jaejun
contributor authorWang, Seunghyeon
date accessioned2026-08-20T21:24:57Z
date available2026-08-20T21:24:57Z
date copyright2025/06/13
date issued2025
identifier otherJCCEE5.CPENG-6677.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314415
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 ...
publisherAmerican Society of Civil Engineers
titleFault Diagnosis of Air Handling Units in an Auditorium Using Real Operational Labeled Data across Different Operation Modes
typeJournal Article
journal volume39
journal issue5
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-6677
journal fristpage04025065-1
journal lastpage04025065-18
page18
treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 005
contenttypeFulltext


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