Journal article
Bayesian network based FDD strategy for variable air volume terminals
Automation in construction, v 41
01 May 2014
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper presents a diagnostic Bayesian network (DBN) for fault detection and diagnosis (FDD) of variable air volume (VAV) terminals. The structure of the DBN illustrates qualitatively the casual relationships between faults and symptoms. The parameters of the DBN describe quantitatively the probabilistic dependences between faults and evidence. The inputs of the DBN are the evidences which can be obtained from measurements in building management systems (BMSs) and manual tests. The outputs are the probabilities of faults concerned. Two rules are adopted to isolate the fault on the basis of the fault probabilities to improve the robustness of the method. Compared with conventional rule-based FDD methods, the proposed method can work well with uncertain and incomplete information, because the faults are reported with probabilities rather than in the Boolean format. Evaluations are made on a dynamic simulator of a VAV air-conditioning system serving an office space using TRNSYS. The results show that it can correctly diagnose ten typical VAV terminal faults. (C) 2013 Elsevier B.V. All rights reserved.
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Details
- Title
- Bayesian network based FDD strategy for variable air volume terminals
- Creators
- Fu Xiao - Hong Kong Polytechnic UniversityYang Zhao - Hong Kong Polytechnic UniversityJin Wen - Drexel UniversityShengwei Wang - Hong Kong Polytechnic University
- Publication Details
- Automation in construction, v 41
- Publisher
- Elsevier
- Number of pages
- 13
- Grant note
- 5332/12E / Research Grants Council (RGC) of the Hong Kong SAR; Hong Kong Research Grants Council
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000334139500012
- Scopus ID
- 2-s2.0-84898015943
- Other Identifier
- 991019168180804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Construction & Building Technology
- Engineering, Civil