NDLI: Overview on diagnosis methods using artificial intelligence application of fuzzy Petri nets

Content Provider	IEEE Xplore Digital Library
Author	Monnin, M. Racoceanu, D. Zerhouni, N.
Copyright Year	2004
Description	Author affiliation: Lab. d'Automatique de Besancon, UMR CNRS 6596, Besancon, France (Monnin, M.; Racoceanu, D.; Zerhouni, N.)
Abstract	This paper studies diagnosis-aid systems that use artificial intelligence tools. This kind of system is very interesting in an uncertain industrial environment, especially flexible production systems. An overview of the most important artificial intelligence diagnosis tools is given. For each tool, we focus on diagnosis principles and its advantages and disadvantages. That allows us to extract four important points that a diagnosis tool should fulfil. Using these results, we propose a tool based on fuzzy Petri nets which allows to make a diagnosis using a model that is easy to build and that takes into account the uncertainties of maintenance knowledge. This tool provides abductive approaches of a fault propagation system with efficient localization and characterization of the fault origin. We apply our tool to an illustrative example of flexible system diagnosis.
Starting Page	740
Ending Page	745
File Size	1506088
Page Count	6
File Format	PDF
ISBN	0780386450
DOI	10.1109/RAMECH.2004.1438010
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-01
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial intelligence Petri nets Fault diagnosis Uncertainty Monitoring Pattern recognition Fuzzy systems Production systems Fault detection Logic
Content Type	Text
Resource Type	Article

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