Dynamic bayesian network-based fault diagnosis for ASHRAE guideline 36: High performance sequence of operation for HVAC systems

Ojas Pradhan, Jin Wen, Yimin Chen, Xing Lu, Mengyuan Chu, Yangyang Fu, Zheng O'Neill, Teresa Wu, K. Selcuk Candan

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

Abstract

A dynamic Bayesian Network (DBN) is proposed in this study to diagnose faults for building heating, ventilating, and air-conditioning (HVAC) systems that are controlled based on American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE)'s Guideline 36: High Performance Sequence of Operation for HVAC (hereinafter Guideline 36). Guideline 36 provides recommendations on supervisory-level control. HVAC systems that adopt these strategies have more comprehensive setpoint reset schedules and more advanced control logics than typical HVAC systems. It is hence of interest to understand how faults might affect the performance of HVAC systems that are controlled based on Guideline 36 and whether we can develop strategies to diagnose and isolate faults even for systems with such comprehensive control sequences. Contrarily to a Bayesian Network (BN), DBN method incorporates the temporal dependencies of fault nodes between time steps using temporal conditional probabilities. This allows fault beliefs to accumulate over time and thus improves diagnosis accuracy. In this study, the accuracy and scalability of the proposed method is evaluated using the data from a Modelica-based simulated testbed. Overall, the developed DBN shows good potential in diagnosing and isolating the root fault causes for HVAC systems that are controlled based on the Guideline 36 control sequence.

Original languageEnglish (US)
Title of host publicationBuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments
PublisherAssociation for Computing Machinery, Inc
Pages365-368
Number of pages4
ISBN (Electronic)9781450391146
DOIs
StatePublished - Nov 17 2021
Event8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021 - Virtual, Online, Portugal
Duration: Nov 17 2021Nov 18 2021

Publication series

NameBuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments

Conference

Conference8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021
Country/TerritoryPortugal
CityVirtual, Online
Period11/17/2111/18/21

Keywords

  • bayesian network
  • dynamic bayesian network
  • fault diagnosis

ASJC Scopus subject areas

  • Information Systems
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Building and Construction
  • Computer Networks and Communications
  • Architecture

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