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A New Diagnostic Method Using Probabilistic Temporal Fault Models
Kazuo HASHIMOTO Kazunori MATSUMOTO Norio SHIRATORI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2002/03/01
Print ISSN: 0916-8532
Type of Manuscript: INVITED PAPER (Special Issue on the 2000 IEICE Excellent Paper Award)
Category: Artificial Intelligence,Cognitive Science
model-based diagnosis, fault model, probabilistic temporal logic, Akaike information criterion,
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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.