Conference proceeding
Dynamic Bayesian Network for Fault Diagnosis
ASHRAE TRANSACTIONS 2021, VOL 127, PT 2, Vol.127(2), pp.6-9
01 Jan 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Dynamic Bayesian Network for Fault Diagnosis
- Creators
- Ojas Pradhan - Drexel UniversityJin Wen - Drexel UniversityYimin Chen - Lawrence Berkeley Natl Lab LBNL, Bldg Technol & Urban Syst Div, Berkeley, CA USATeresa Wu - Arizona State UniversityZheng O'Neill - Texas A&M Univ, Dept Mech Engn, College Stn, TX USAASHRAE
- Publication Details
- ASHRAE TRANSACTIONS 2021, VOL 127, PT 2, Vol.127(2), pp.6-9
- Series
- ASHRAE Transactions
- Publisher
- Amer Soc Heating, Refrigerating And Air-Conditioning Engs
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Identifiers
- 991021960806704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Construction & Building Technology
- Engineering, Mechanical
- Instruments & Instrumentation