Qualitative diagnosis of condition systems

Jeffrey Ashley; Lawrence E. Holloway

doi:10.1023/B:DISC.0000039787.51382.af

Qualitative diagnosis of condition systems

Jeffrey Ashley, Lawrence E. Holloway

Institute for Sustainable Manufacturing (ISM)

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

A condition system is a collection of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In this paper, a system fault is defined in terms of observed behavior versus expected behavior, where the expected behavior is defined through condition system models. A diagnosis of this fault localizes the subsystem that is the source of the discrepancy between output and expected observations. We show that the structure of the interacting subsystems define a diagnostic causal model that captures the causal structure of subsystem dependencies. The diagnostic causal model can then be used to determine a set of subsystems that might be the source of a fault.

Original language	English
Pages (from-to)	395-412
Number of pages	18
Journal	Discrete Event Dynamic Systems: Theory and Applications
Volume	14
Issue number	4
DOIs	https://doi.org/10.1023/B:DISC.0000039787.51382.af
State	Published - Oct 2004

Bibliographical note

Funding Information:
This work has been supported in part by Rockwell International, NSF grant ECS-9807106 and ECS-0115694, the Office of Naval Research under the grant N000140110621, and the Center for Manufacturing at the University of Kentucky.

Keywords

Causal nets
Discrete event systems
Fault diagnosis
Petri nets

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Electrical and Electronic Engineering

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10.1023/B:DISC.0000039787.51382.af

Cite this

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title = "Qualitative diagnosis of condition systems",

abstract = "A condition system is a collection of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In this paper, a system fault is defined in terms of observed behavior versus expected behavior, where the expected behavior is defined through condition system models. A diagnosis of this fault localizes the subsystem that is the source of the discrepancy between output and expected observations. We show that the structure of the interacting subsystems define a diagnostic causal model that captures the causal structure of subsystem dependencies. The diagnostic causal model can then be used to determine a set of subsystems that might be the source of a fault.",

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AU - Holloway, Lawrence E.

N1 - Funding Information: This work has been supported in part by Rockwell International, NSF grant ECS-9807106 and ECS-0115694, the Office of Naval Research under the grant N000140110621, and the Center for Manufacturing at the University of Kentucky.

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N2 - A condition system is a collection of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In this paper, a system fault is defined in terms of observed behavior versus expected behavior, where the expected behavior is defined through condition system models. A diagnosis of this fault localizes the subsystem that is the source of the discrepancy between output and expected observations. We show that the structure of the interacting subsystems define a diagnostic causal model that captures the causal structure of subsystem dependencies. The diagnostic causal model can then be used to determine a set of subsystems that might be the source of a fault.

AB - A condition system is a collection of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In this paper, a system fault is defined in terms of observed behavior versus expected behavior, where the expected behavior is defined through condition system models. A diagnosis of this fault localizes the subsystem that is the source of the discrepancy between output and expected observations. We show that the structure of the interacting subsystems define a diagnostic causal model that captures the causal structure of subsystem dependencies. The diagnostic causal model can then be used to determine a set of subsystems that might be the source of a fault.

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KW - Discrete event systems

KW - Fault diagnosis

KW - Petri nets

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Qualitative diagnosis of condition systems

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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