Sequential window diagnoser for discrete-event systems under unreliable observations

Authors:
Wen-Chiao Lin;Humberto E. Garcia;David Thorsley;Tae-Sic Yoo
Affiliations:
-;-;-;-
Venue:
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Year:
2009

Citing 5
Cited 1

State Observation and Diagnosis of Discrete-Event SystemsDescribed by Stochastic Automata

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Diagnosis of Intermittent Faults

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Introduction to Discrete Event Systems

Introduction to Discrete Event Systems
Event Counting of Partially-Observed Discrete-Event Systems with Uniformly and Nonuniformly Bounded Diagnosis Delays

Discrete Event Dynamic Systems
Model-based detection of routing events in discrete flow networks

Automatica (Journal of IFAC)

A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

Discrete Event Dynamic Systems

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Abstract

This paper addresses the issue of counting the occurrence of special events in the framework of partially-observed discrete-event dynamical systems (DEDS). Developed diagnosers referred to as sequential window diagnosers (SWDs) utilize the stochastic diagnoser probability transition matrices developed in [9] along with a resetting mechanism that allows on-line monitoring of special event occurrences. To illustrate their performance, the SWDs are applied to detect and count the occurrence of special events in a particular DEDS. Results show that SWDs are able to accurately track the number of times special events occur.