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Genetic optimisation of a neural network damage diagnostic

Published online by Cambridge University Press:  03 February 2016

G. Manson ,
E. Papatheou  and
K. Worden
G. Manson
Affiliation:
Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
E. Papatheou
Affiliation:
Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
K. Worden
Affiliation:
Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
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Abstract

This paper presents an automated optimisation procedure for the feature selection stage of a previously proposed structural health monitoring methodology using a genetic algorithm. The same diagnostic is used in the attempt to progress up the levels of damage detection to location and severity. It was validated experimentally on a Gnat aircraft wing. An artificial neural network is used as a classifier and the work is compared with the previous selection strategy based on engineering judgement.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 112 , Issue 1131 , May 2008 , pp. 267 - 274
Copyright
Copyright © Royal Aeronautical Society 2008 

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