NDLI: A fast model-based diagnosis engine

Content Provider	IEEE Xplore Digital Library
Author	Fijany, A. Barrett, A.C. Vatan, F.
Copyright Year	2012
Description	Author affiliation: Jet Propulsion Laboratory, 4800 Oak Grove Dr. Pasadena, CA 91109, USA (Barrett, A.C.; Vatan, F.) \|\| Italian Institute of Technology, Via Morego 30, 16163 Genoa, Italy (Fijany, A.)
Abstract	In this paper we present a novel fast model-based diagnosis engine. Our novel engine is based on a two-step approach to diagnosis, i.e., off-line system analysis and on-line diagnosis. The efficiency of our novel method results from the fact that, by performing a detailed analysis of the target system, it drastically reduces the amount of computation needed for diagnosis. In particular, our new algorithm relies on the concept and use of minimal set of ARRs to achieve a much better efficiency in the diagnosis process. Our novel diagnosis engine is based on our two recent results. First, it uses our recently developed method for generation of the complete set of ARRs. Second, it uses the minimal set of ARRs; as we have recently shown that for any given number of faults, i.e., single, double, triple, etc., there is a corresponding minimal set of ARRs which is usually significantly smaller than the complete set of ARRs. We present and discuss the performance of our diagnosis engine by its application to several examples. We show that, even by using a non-exoneration assumption, we achieve a much better efficiency over the GDE as well as full ARR-based approaches for model-based diagnosis.
Starting Page	1
Ending Page	11
File Size	1077100
Page Count	11
File Format	PDF
ISBN	9781457705564
ISSN	1095323X
e-ISBN	9781457705571
DOI	10.1109/AERO.2012.6187367
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Vectors Engines Sensor systems Analytical models Complexity theory
Content Type	Text
Resource Type	Article
Subject	Aerospace Engineering Space and Planetary Science

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