DocumentCode :
1812442
Title :
Sensor placement for fault diagnosis using genetic algorithm
Author :
Guoyi Chi ; Danwei Wang ; Ming Yu ; Alavi, Meysam ; Tung Le ; Ming Luo
Author_Institution :
Centre for E-City, Nanyang Technol. Univ., Singapore, Singapore
fYear :
2012
fDate :
17-21 Sept. 2012
Firstpage :
1
Lastpage :
7
Abstract :
This paper presents a novel methodology for the purpose of fault detection and isolation (FDI) to a two-tank system. This new methodology benefits from the basic facts that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will cause the corresponding ARRs to change. Based on these facts, the minimal isolation set as an important concept is introduced to make each fault in the fault set F detectable and isolable. Then, the sensor placement problem consists in determining an optimal minimal isolation set associated with the least number of sensors. A dedicated genetic algorithm is developed to solve the formulated sensor placement problem. A case study of a two-tank system shows that the proposed methodology performs well.
Keywords :
condition monitoring; fault diagnosis; genetic algorithms; sensor placement; tanks (containers); tanning; analytical redundancy relations; fault diagnosis; genetic algorithm; optimal minimal isolation; sensor placement; two tank system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technologies & Factory Automation (ETFA), 2012 IEEE 17th Conference on
Conference_Location :
Krakow
ISSN :
1946-0740
Print_ISBN :
978-1-4673-4735-8
Electronic_ISBN :
1946-0740
Type :
conf
DOI :
10.1109/ETFA.2012.6489615
Filename :
6489615
Link To Document :
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