Journal article
Diagnostic Bayesian networks for diagnosing air handling units faults - Part II: Faults in coils and sensors
Applied thermal engineering, v 90
05 Nov 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This is the second part of a study on diagnostic Bayesian networks (DBNs)-based method for diagnosing faults in air handling units (AHUs) in buildings. In this part, 4 DBNs are developed to diagnose faults in heating/cooling coils, sensors and faults in secondary supply chilled water/heating water systems. There are 18 typical faults concerned and 35 fault detectors introduced. The DBNs are developed mainly on the basis of first principles and fault patterns resulted from literature and three AHU fault detection and diagnosis (FDD) projects. Efficient fault detection rules/methods from a comprehensive literature survey are integrated into the DBNs. Also, some new fault detection rules are developed. The 4 DBNs were evaluated using experimental data from ASHRAE Project RP-1312. Results show that the proposed DBNs effectively diagnosed AHU faults. (C) 2015 Elsevier Ltd. All rights reserved.
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Details
- Title
- Diagnostic Bayesian networks for diagnosing air handling units faults - Part II: Faults in coils and sensors
- Creators
- Yang Zhao - Eindhoven University of TechnologyJin Wen - Drexel UniversityShengwei Wang - Hong Kong Polytech Univ, Dept Bldg Serv Engn, Hong Kong, Hong Kong, Peoples R China
- Publication Details
- Applied thermal engineering, v 90
- Publisher
- Elsevier
- Number of pages
- 13
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000364246500016
- Scopus ID
- 2-s2.0-84937389469
- Other Identifier
- 991019169536804721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Energy & Fuels
- Engineering, Mechanical
- Mechanics
- Thermodynamics