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A New Diagnostic Method Using Probabilistic Temporal Fault Models

Kazuo HASHIMOTO, Kazunori MATSUMOTO, Norio SHIRATORI

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Summary :

This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. First, a fault model is defined by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose Akaike information criterion (AIC) is minimal. It is proved by simulation that the AIC-based hypothesis selection achieves a high precision in diagnosis.

Publication
IEICE TRANSACTIONS on Information Vol.E85-D No.3 pp.444-454
Publication Date
2002/03/01
Publicized
Online ISSN
DOI
Type of Manuscript
Special Section INVITED PAPER (Special Issue on the 2000 IEICE Excellent Paper Award)
Category
Artificial Intelligence,Cognitive Science

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