Diagnostic Bayesian networks for diagnosing air handling units faults - Part II: Faults in coils and sensors

Yang Zhao, Jin Wen, Shengwei Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

72 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)145-157
Number of pages13
JournalApplied Thermal Engineering
Volume90
DOIs
Publication statusPublished - 22 Jul 2015

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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