Bayesian network based FDD strategy for variable air volume terminals

Fu Xiao, Yang Zhao, Jin Wen, Shengwei Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

107 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)106-118
Number of pages13
JournalAutomation in Construction
Publication statusPublished - 1 Jan 2014


  • Bayesian network
  • Fault detection
  • Fault diagnosis
  • VAV terminal

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


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