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Dynamic Bayesian Networks for Fault Prognosis

Conference · · Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
 [1];  [2];  [3];  [4]
  1. Drexel University, United States of America; Drexel University
  2. Drexel University, United States of America
  3. Texas A&M University, USA
  4. Texas A&M University, United States of America
+ Show Author Affiliations

A dynamic Bayesian Network (DBN)-based fault prognosis framework is proposed in this study to predict the future fault probabilities of gradual faults. The proposed framework utilizes the trend in prediction error generated from data driven forecasting models to estimate the future fault beliefs. The accuracy and scalability of the proposed method is evaluated using the data from a Modelica-based virtual testbed. Overall, the developed framework demonstrates good potential in estimating future fault probabilities of gradual faults.

Research Organization:
Texas A&M Engineering Experiment Station
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office (BTO)
DOE Contract Number:
EE0009150
OSTI ID:
2331279
Journal Information:
Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Journal Name: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
Country of Publication:
United States
Language:
English

References (3)

Analysis of an information monitoring and diagnostic system to improve building operations journal October 2001
A review of data-driven fault detection and diagnostics for building HVAC systems journal June 2023
Data-driven based fault prognosis for industrial systems: a concise overview journal March 2020

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