Feature selection for a neural network damage diagnostic using a genetic algorithm

被引：0
|
作者：
Manson, G. [1 ]
Worden, K. [2 ]
机构：
[1] Univ Sheffield, Dynam Res Grp, Dept Mech Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England
[2] Univ Sheffield, Dept Mech Engn, Dynam Res Grp, Sheffield S1 3JD, S Yorkshire, England
来源：
PROCEEDINGS OF THE THIRD EUROPEAN WORKSHOP STRUCTURAL HEALTH MONITORING 2006 | 2006年
关键词：
D O I：
暂无
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
A critical problem in Structural Health Monitoring (SHM) based on pattern recognition methods, is the correct selection of features i.e. measured and processed data for the diagnosis. Various examples of selection strategies have been tried in the past and one approach that has proved effective is the use of combinatorial optimisation schemes. The current paper presents a case study based on an SHM scheme for damage severity assessment in an aircraft wing. The feature selection used is based on a genetic algorithm and the regression model is an artificial neural network. Comparisons are made with the results obtained when the features are selected on the basis of Engineering judgement.
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页码：683 / +
页数：2
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