Simultaneous presents faults detection by using Diagnostic Bayesian Network in Air Handling Units

Z. Wang*, C.J. Lu, Martín Mosteiro-Romero, L.C.M. Itard

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Energy waste in buildings can range from 5% to 30% due to faults and inadequate controls. To effectively mitigate energy waste and reduce maintenance costs, the development of Fault Detection and Diagnosis (FDD) algorithms for building energy systems is crucial. Diagnostic Bayesian Networks (DBNs), as graphical probability models, are particularly useful in scenarios where high-quality data is not always available. While many studies have focused on single fault detection using DBNs, the occurrence of multiple simultaneous faults is common, yet the versatility of DBNs in handling such cases is rarely explored. This study adapts a DBN, initially designed for single fault diagnosis, to perform simultaneous fault diagnosis Experiments were conducted on an air handling unit (AHU) in the Netherlands, using implemented simultaneous faults to test the model. The results suggest that the DBN can detect both single and multiple faults effectively.
Original languageEnglish
Title of host publicationASim2024, The 5th Asia Conference of the IBPSA
PublisherIBPSA
Pages1613-1620
Number of pages8
Publication statusPublished - 2024
EventThe 5th Asia Conference of International Building Performance Simulation Association 2024 - Osaka, Japan
Duration: 8 Dec 202410 Dec 2024
https://www.asim2024.org/

Conference

ConferenceThe 5th Asia Conference of International Building Performance Simulation Association 2024
Abbreviated titleAsim 2024
Country/TerritoryJapan
CityOsaka
Period8/12/2410/12/24
Internet address

Keywords

  • Fault Detection and Diagnosis
  • Bayesian network
  • Simultaneous faults
  • AHU
  • Diagnostic Bayesian Networks
  • Multiple Faults Detection

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