A new diagnostic method using probabilistic temporal fault models

Kazuo Hashimoto, Kazunori Matsumoto, Norio Shiratori

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)444-454
ページ数11
ジャーナルIEICE Transactions on Information and Systems
E85-D
3
出版ステータスPublished - 2002 3
外部発表はい

ASJC Scopus subject areas

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

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