TY - GEN
T1 - Dynamic bayesian network-based fault diagnosis for ASHRAE guideline 36
T2 - 8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021
AU - Pradhan, Ojas
AU - Wen, Jin
AU - Chen, Yimin
AU - Lu, Xing
AU - Chu, Mengyuan
AU - Fu, Yangyang
AU - O'Neill, Zheng
AU - Wu, Teresa
AU - Selcuk Candan, K.
N1 - Funding Information:
The work presented in the paper has been carried out with the support of National Science Foundation (NSF) under the Partnerships for Innovation – Research Project (PFI-RP): Data – Driven Services for High Performance of Sustainable Buildings (Award no. 2050509).
Publisher Copyright:
© 2021 ACM.
PY - 2021/11/17
Y1 - 2021/11/17
N2 - 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.
AB - 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.
KW - bayesian network
KW - dynamic bayesian network
KW - fault diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85121010253&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121010253&partnerID=8YFLogxK
U2 - 10.1145/3486611.3491124
DO - 10.1145/3486611.3491124
M3 - Conference contribution
AN - SCOPUS:85121010253
T3 - BuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments
SP - 365
EP - 368
BT - BuildSys 2021 - Proceedings of the 2021 ACM International Conference on Systems for Energy-Efficient Built Environments
PB - Association for Computing Machinery, Inc
Y2 - 17 November 2021 through 18 November 2021
ER -