Conference proceeding
Dynamic Bayesian Network-Based Fault Diagnosis for ASHRAE Guideline 36: High Performance Sequence of Operation for HVAC Systems
BUILDSYS'21: PROCEEDINGS OF THE 2021 ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILT ENVIRONMENTS, pp 365-368
01 Jan 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Dynamic Bayesian Network-Based Fault Diagnosis for ASHRAE Guideline 36: High Performance Sequence of Operation for HVAC Systems
- Creators
- Ojas Pradhan - Drexel UniversityJin Wen - Drexel UniversityYimin Chen - Lawrence Berkeley National LaboratoryXing Lu - Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USAMengyuan Chu - Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USAYangyang Fu - Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USAZheng O'Neill - Texas A&M Univ, Dept Mech Engn, College Stn, TX 77843 USATeresa Wu - Arizona State UniversityK. Selcuk Candan - Arizona State UniversityACM
- Publication Details
- BUILDSYS'21: PROCEEDINGS OF THE 2021 ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILT ENVIRONMENTS, pp 365-368
- Publisher
- Assoc Computing Machinery
- Number of pages
- 4
- Grant note
- 2050509 / National Science Foundation (NSF) under the Partnerships for Innovation - Research Project (PFI-RP): Data Driven Services for High Performance of Sustainable Buildings; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000945948100067
- Scopus ID
- 2-s2.0-85121010253
- Other Identifier
- 991021960802304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods
- Construction & Building Technology
- Green & Sustainable Science & Technology