Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors

Yang Zhao, Jin Wen, Fu Xiao, Xuebin Yang, Shengwei Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

71 Citations (Scopus)

Abstract

There is still a lack of effective methods for diagnosing AHU faults automatically. In this study, a diagnostic Bayesian networks (DBNs)-based method is proposed to diagnose 28 faults, which cover most of common faults in AHUs. The basic idea is to fully utilize all diagnostic information in an information fusion way. The DBNs are developed based on a comprehensive survey of AHU fault detection and diagnosis (FDD) methods and fault patterns reported in three AHU FDD projects including NIST 6964, ASHRAE projects RP-1020 and RP-1312. The study is published in two parts. In the Part I, the methodology is described firstly. Four DBNs are developed to diagnose faults in fans, dampers, ducts, filters and sensors. There are 10 typical faults concerned and 14 fault detectors introduced. Evaluations are made using the experimental data from the ASHRAE Project RP-1312. Results show that the DBN-based method is effective in diagnosing faults even when the diagnostic information is uncertain and incomplete.
Original languageEnglish
Pages (from-to)1272-1286
Number of pages15
JournalApplied Thermal Engineering
Volume111
DOIs
Publication statusPublished - 25 Jan 2017

Keywords

  • Air handling unit
  • Bayesian network
  • Fault detection
  • Fault diagnosis

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering

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