Probabilistic modeling of alarm observation delay in network diagnosis

Kazuo Hashimoto, Kazunori Matsumoto, Norio Shiratori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. Firstly, a fault model is denned 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 AIC (Akaike information criterion) is minimal. It is proved that by simulation that the AIC based hypothesis selection achieves the high precision in diagnosis.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages734-744
Number of pages11
Volume1886 LNAI
Publication statusPublished - 2000
Externally publishedYes
Event6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000 - Melbourne, VIC
Duration: 2000 Aug 282000 Sep 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1886 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000
CityMelbourne, VIC
Period00/8/2800/9/1

Fingerprint

Probabilistic Modeling
Akaike Information Criterion
Fault
Model-based Diagnosis
Time series
Rooted Trees
Probable
Observation
Simulation
Model

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Hashimoto, K., Matsumoto, K., & Shiratori, N. (2000). Probabilistic modeling of alarm observation delay in network diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1886 LNAI, pp. 734-744). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1886 LNAI).

Probabilistic modeling of alarm observation delay in network diagnosis. / Hashimoto, Kazuo; Matsumoto, Kazunori; Shiratori, Norio.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1886 LNAI 2000. p. 734-744 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1886 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hashimoto, K, Matsumoto, K & Shiratori, N 2000, Probabilistic modeling of alarm observation delay in network diagnosis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1886 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1886 LNAI, pp. 734-744, 6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000, Melbourne, VIC, 00/8/28.
Hashimoto K, Matsumoto K, Shiratori N. Probabilistic modeling of alarm observation delay in network diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1886 LNAI. 2000. p. 734-744. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Hashimoto, Kazuo ; Matsumoto, Kazunori ; Shiratori, Norio. / Probabilistic modeling of alarm observation delay in network diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1886 LNAI 2000. pp. 734-744 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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