Optimal sensor allocation for system abnormality detection by integrating causal models and set-covering algorithms

Jing Li; Jin Jionghua

Optimal sensor allocation for system abnormality detection by integrating causal models and set-covering algorithms

Jing Li, Jin Jionghua

Industrial, Systems and Operations Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

This paper focuses on optimal sensor allocation in Distributed Sensor Networks (DSN) for complex systems which are represented by Bayesian network (BN) models. The paper presents a new strategy to transfer the optimal sensor allocation problem into a set-covering optimization problem. Furthermore, the efficiency of the search algorithm is improved by integration of BN models and the greedy search algorithm. Case studies on two manufacturing processes are conducted, which demonstrates the effectiveness of the proposed method in abnormality detection.

Original language	English (US)
Title of host publication	IIE Annual Conference and Expo 2008
Pages	887-892
Number of pages	6
State	Published - 2008
Event	IIE Annual Conference and Expo 2008 - Vancouver, BC, Canada Duration: May 17 2008 → May 21 2008

Other

Other	IIE Annual Conference and Expo 2008
Country/Territory	Canada
City	Vancouver, BC
Period	5/17/08 → 5/21/08

Keywords

Bayesian networks
Causal models
Sensor allocation
Set-covering algorithm

ASJC Scopus subject areas

Computer Science Applications
Software
Industrial and Manufacturing Engineering

Cite this

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title = "Optimal sensor allocation for system abnormality detection by integrating causal models and set-covering algorithms",

abstract = "This paper focuses on optimal sensor allocation in Distributed Sensor Networks (DSN) for complex systems which are represented by Bayesian network (BN) models. The paper presents a new strategy to transfer the optimal sensor allocation problem into a set-covering optimization problem. Furthermore, the efficiency of the search algorithm is improved by integration of BN models and the greedy search algorithm. Case studies on two manufacturing processes are conducted, which demonstrates the effectiveness of the proposed method in abnormality detection.",

keywords = "Bayesian networks, Causal models, Sensor allocation, Set-covering algorithm",

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year = "2008",

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booktitle = "IIE Annual Conference and Expo 2008",

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AU - Jionghua, Jin

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AB - This paper focuses on optimal sensor allocation in Distributed Sensor Networks (DSN) for complex systems which are represented by Bayesian network (BN) models. The paper presents a new strategy to transfer the optimal sensor allocation problem into a set-covering optimization problem. Furthermore, the efficiency of the search algorithm is improved by integration of BN models and the greedy search algorithm. Case studies on two manufacturing processes are conducted, which demonstrates the effectiveness of the proposed method in abnormality detection.

KW - Bayesian networks

KW - Causal models

KW - Sensor allocation

KW - Set-covering algorithm

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M3 - Conference contribution

AN - SCOPUS:63849265475

SP - 887

EP - 892

BT - IIE Annual Conference and Expo 2008

T2 - IIE Annual Conference and Expo 2008

Y2 - 17 May 2008 through 21 May 2008

ER -

Optimal sensor allocation for system abnormality detection by integrating causal models and set-covering algorithms

Abstract

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Keywords

ASJC Scopus subject areas

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Cite this