Authors:
Mario Thron
;
Thomas Bangemann
and
Nico Suchold
Affiliation:
Institut für Automation und Kommunikation, Germany
Keyword(s):
Industrial diagnosis, expert system, Fuzzy Logic, Bayesian Network, logic description, modularity.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Enterprise Information 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
;
Knowledge-Based Systems Applications
;
Symbolic Systems
Abstract:
Expert systems are well known tools for diagnosis purposes in medicine and industry. One problem is the hard efford, to create the knowledge base. This article describes an expert system for industrial diagnosis and shows an efficient approach for the creation of the rule base, which is based on the reusage of knowledge modules. These knowledge modules are representants for assets like devices, machines and plants. The article encourages manufacturers of such assets to provide diagnosis knowledge bases by using a proposed multi-paradigm rule definition language called HLD (Hybrid Logic Description).
Rule based knowledge may be expressed by using various methodologies, which differ in expressiveness but also in runtime performance. The HLD allowes rules to be defined as propositional logic with or without the use of certainty factors, as Fuzzy Logic or as probabilistic rules as in Bayesian Networks. The most effective rule type may be chosen to describe causal dependencies between sy
mptoms and failures. An evaluation prototype implementation has been developed in the research project WISA, which includes a sotftware tool chain for handling HLD files.
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