Selecting observation time in the monitoring and interpretation of time-varying data

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Abstract

A lot of previous approaches to monitoring involved a continuous reading of the system parameters in order to recognize when anomalies in the behavior of the system under examination can trigger the diagnostic process. This paper deals with the application of Markov chain theory to the selection of observation time in the monitoring and diagnosis of time-varying systems. The goal of the present paper is to show how, by assuming a framework where the temporal behavior of the components of the system is modeled in a stochastic way, the continuous observation of critical parameters can be avoided; indeed, this kind of approach allows us to get a useful criterion for choosing observation time in domains where getting observations can be expensive. Observations are then requested only when the necessity for a diagnostic process becomes relevant and a focusing on the components that are more likely to be faulty can also be achieved.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 3rd Congress of the Italian Association for Artificial Intelligence, AI*IA 1993, Proceedings
EditorsPietro Torasso
PublisherSpringer Verlag
Pages314-325
Number of pages12
ISBN (Print)9783540572923
DOIs
Publication statusPublished - 1993
Externally publishedYes
Event3rd Congress of the Italian Association for Artificial Intelligence, AI*IA 1993 - Torino, Italy
Duration: 26 Oct 199328 Oct 1993

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume728 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Congress of the Italian Association for Artificial Intelligence, AI*IA 1993
Country/TerritoryItaly
CityTorino
Period26/10/9328/10/93

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