Dynamic Bayesian Network for Fault Diagnosis

Ojas Pradhan, Jin Wen, Yimin Chen, Teresa Wu, Zheng O'Neill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


A comparative study between using a dynamic Bayesian network (DBN) against using a static Bayesian network (BN) for building heating ventilating, and air conditioning fault diagnosis (HVAC) is presented. Contrarily to a static BN, DBN method incorporates temporal dependencies between fault nodes between timesteps using temporal conditional probabilities. This allows fault beliefs to accumulate over time and hence improves the diagnosis accuracy. The two methods are evaluated using real building data obtained from a campus building. Overall, the DBN showed improved fault belief when diagnosing and isolating faults across various components and sub-systems. Sensitivity tests on the temporal conditional probabilities for DBN showed that the model is robust.

Original languageEnglish (US)
Title of host publicationASHRAE Virtual Annual Conference, ASHRAE 2021
Number of pages4
ISBN (Electronic)9781955516006
StatePublished - 2021
Externally publishedYes
Event2021 ASHRAE Virtual Annual Conference, ASHRAE 2021 - Virtual, Online
Duration: Jun 28 2021Jun 30 2021

Publication series

NameASHRAE Transactions


Conference2021 ASHRAE Virtual Annual Conference, ASHRAE 2021
CityVirtual, Online

ASJC Scopus subject areas

  • Building and Construction
  • Mechanical Engineering


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