Author:
Gustavo Arroyo Figueroa
Affiliation:
Electrical Power Research Institute, Mexico
Keyword(s):
Bayesian networks, temporal reasoning, uncertainty, diagnosis, thermal power plants.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Decision Support Systems
;
Expert Systems
;
Health Information Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Many real-world domains, such as industrial diagnosis, require an adequate representation that combines
uncertainty and time. Research in this field involves the development of new knowledge representation and inference mechanisms to deal with uncertainty and time. Current temporal probabilistic models become too complex when used for real world applications. In this paper, we propose a model, Temporal Events Bayesian Networks (TEBN), based on a natural extension of a simple Bayesian network. TEBN tries to make a balance between expressiveness and computational efficiency. Based on a temporal node definition, causal-temporal dependencies are represented by qualitative and quantitative relations, using different time intervals within each variable (multiple granularity). Qualitative knowledge about temporal relations between variables is used to facilitate the acquisition of the quantitative parameters. The inference mechanism combines qualitative and quantitative reasoning. The propo
sed approach is applied to a thermal power plant through a detailed case study, with promising results.
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