Authors:
Luigi Troiano
1
and
Davide De Pasquale
2
Affiliations:
1
University of Sannio, Italy
;
2
Intelligentia s.r.l., Italy
Keyword(s):
Troubleshooting, Bayesian networks, Maintenance, Decision support tools.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Enterprise Information Systems
;
Industrial Applications of Artificial Intelligence
;
Information Engineering Methodologies
;
Information Systems Analysis and Specification
Abstract:
Troubleshooting complex systems, such as industrial plants and machinery, is a task entailing an articulated decision making process hard to structure, and generally relying on human experience. Recently probabilistic reasoning, and Bayesian networks in particular, proved to be an effective means to support and drive decisions in Troubleshooting. However, troubleshooting a real system requires to face scalability and feasibility issues, so that the direct employment of Bayesian networks is not feasible. In this paper we report our experience in applying Bayesian approach to industrial case and we propose a methodology to decompose a complex problem in more treatable parts.