A new diagnostic method using probabilistic temporal fault models

Kazuo Hashimoto, Kazunori Matsumoto, Norio Shiratori

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)444-454
Number of pages11
JournalIEICE Transactions on Information and Systems
VolumeE85-D
Issue number3
Publication statusPublished - 2002 Mar

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Keywords

  • Akaike information criterion
  • Fault model
  • Model-based diagnosis
  • Probabilistic temporal logic

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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