Conference proceeding
Dynamic Bayesian Networks for Fault Prognosis
Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp 296-297
15 Nov 2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Dynamic Bayesian Networks for Fault Prognosis
- Creators
- Ojas Pradhan - Drexel UniversityJin Wen - Drexel UniversityMengyuan Chu - Texas A&M UniversityZheng O'Neill - Texas A&M University, United States of AmericaACM
- Publication Details
- Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp 296-297
- Conference
- BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
- Series
- ACM Other Conferences
- Publisher
- ACM
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:001147988300050
- Scopus ID
- 2-s2.0-85179508399
- Other Identifier
- 991021960642404721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Construction & Building Technology