Model-based diagnosability and sensor placement application to a frame 6 gas turbine subsystem

Authors:
Louise Travé-Massuyès;Teresa Escobet;Robert Milne
Affiliations:
LAAS, CNRS, LEA-SICA, Toulouse, France;UPC and LEA-SICA, Automatic Control Department, Universitat Politècnica de Catalunya, Terrassa, Barcelona, Spain;Intelligent Applications Ltd., Livingston, West Lothian, Scotland
Venue:
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Year:
2001

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Abstract

This paper presents a methodology for: assessing the degree of diagnosability of a system, i.e. given a set of sensors, which faults can be discriminated? and; characterising and determining the minimal additional sensors which guarantee a specified degree of diagnosability. This method has been applied to several subsystems of a Ge neral Electric Frame 6 gas turbine owned by a major UK utility.