Optimal sensor allocation by integrating causal models and set-covering algorithms

Jing Li, Jionghua Jin

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Massive amounts of data are generated in Distributed Sensor Networks (DSNs), posing challenges to effective and efficient detection of system abnormality through data analysis. This article proposes a new method for optimal sensor allocation in a DSN with the objective of timely detection of the abnormalities in a underlying physical system. This method involves two steps: first, a Bayesian Network (BN) is built to represent the causal relationships among the physical variables in the system; second, an integrated algorithm by combining the BN and a set-covering algorithm is developed to determine which physical variables should be sensed, in order to minimize the total sensing cost as well as satisfy a prescribed detectability requirement. Case studies are performed on a hot forming process and a large-scale cap alignment process, showing that the developed algorithm satisfies both the cost and detectability requirements.

Original languageEnglish (US)
Pages (from-to)564-576
Number of pages13
JournalIIE Transactions (Institute of Industrial Engineers)
Volume42
Issue number8
DOIs
StatePublished - Aug 1 2010

Keywords

  • Bayesian networks
  • Causal models
  • Sensor allocation
  • Set-covering algorithm

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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