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 language | English (US) |
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Pages (from-to) | 564-576 |
Number of pages | 13 |
Journal | IIE Transactions (Institute of Industrial Engineers) |
Volume | 42 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2010 |
Keywords
- Bayesian networks
- Causal models
- Sensor allocation
- Set-covering algorithm
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering