A fault detection and diagnosis strategy of VAV air-conditioning systems for improved energy and control performances

Jianying Qin, Shengwei Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

74 Citations (Scopus)

Abstract

This paper presents the results of a site survey study on the faults in variable air volume (VAV) terminals and an automatic fault detection and diagnosis (FDD) strategy for VAV air-conditioning systems using a hybrid approach. The site survey study was conducted in a commercial building. 20.9% VAV terminals were ineffective and 10 main faults were identified in the VAV air-conditioning systems. The FDD strategy adopts a hybrid approach utilizing expert rules, performance indexes and statistical process control models to address these faults. Supported by a pattern recognition method, expert rules and performance indexes based on system physical characteristics are adopted to detect 9 of the 10 faults. Two pattern recognition indexes are introduced for fault isolation to overcome the difficulty in differentiating damper sticking and hysteresis from improper controller tuning. A principal component analysis (PCA)-based method is developed to detect VAV terminal flow sensor biases and to reconstruct the faulty sensors. The FDD strategy is tested and validated on typical VAV air-conditioning systems involving multiple faults both in simulation and in situ tests.
Original languageEnglish
Pages (from-to)1035-1048
Number of pages14
JournalEnergy and Buildings
Volume37
Issue number10
DOIs
Publication statusPublished - 1 Oct 2005

Keywords

  • Commissioning
  • Fault detection and diagnosis
  • Principal component analysis
  • Variable air volume system
  • VAV terminal

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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