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Optimal Sensor Allocation for Fault Detection and IsolationAutomatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the fault diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.
Document ID
Document Type
Conference Paper
Azam, Mohammad (Connecticut Univ. Storrs, CT, United States)
Pattipati, Krishna (Connecticut Univ. Storrs, CT, United States)
Patterson-Hine, Ann (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 23, 2013
Publication Date
January 1, 2004
Publication Information
ISSN: 1062-922X
ISBN: 1062-922X
Subject Category
Systems Analysis and Operations Research
Meeting Information
2004 IEEE International Conference on Systems, Man and Cybernetics, Volume 2(Hague)
Funding Number(s)
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