Hostname: page-component-84b7d79bbc-g5fl4 Total loading time: 0 Render date: 2024-07-26T00:48:54.706Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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.

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Doherty, J.E., Nondestructive evaluation, Chapter 12 in Handbook on Experimental Mechanics, 1987, Kobayashi, A.S. (Ed), Society for Experimental Mechanics.Google Scholar
2. Doebling, S.W., Farrar, C.R. and Prime, M.B., A summary review of vibration-based damage identification methods, 1998, The Shock and Vibration Digest, 30, pp 91–105.Google Scholar
3. Pai, P.F. and Jin, S., Locating structural damage by detecting boundary effects, 2000, J Sound and Vibration, 231, (4), pp 1079–110.Google Scholar
4. Rytter, A., Vibration-Based Inspection of Civil Engineering Structures, PhD Thesis, 1993, Department of Building Technology and Structural Engineering, University of Aalborg, Denmark.Google Scholar
5. Worden, K., Manson, G. and Allman, D.J., Experimental validation of a structural health monitoring methodology, Part I: Novelty detection on a laboratory structure, 2003, J Sound and Vibration, 259, (2), pp 323–343.Google Scholar
6. Manson, G., Worden, K. and Allman, D.J., Experimental validation of a structural health monitoring methodology, Part II: Novelty detection on a Gnat aircraft, 2003, J Sound and Vibration, 259, (2), pp 345–363.Google Scholar
7. Manson, G., Worden, K. and Allman, D.J., Experimental validation of a structural health monitoring methodology, Part III: Damage location on a Gnat aircraft wing, 2003, J sound and Vibration, 259, (2), pp 365–385.Google Scholar
8. Manson, G., Worden, K. and Allman, D.J., Experimental validation of a damage severity method, 2002, Proceedings of 1st European Workshop on Structural Health Monitoring, Paris, France, pp 845–852.Google Scholar
9. Worden, K., Hilson, K. and Manson, G., Genetic optimisation of a neural Damage Locator, Submitted to J Sound and Vibration.Google Scholar
10. Worden, K. and Burrows, A.P., Optimal sensor placement for fault detection, 2001, Engineering Structures, 23, pp 885–901.Google Scholar
11. Jack, L.B. and Nandi, A.K., Genetic algorithms for feature selection in machine condition monitoring with vibration signals, 2000, IEE Proc – Vis Image Signal Process, 147, (3), pp 205–212.Google Scholar
12. Barnett, V. and Lewis, T., Outliers in Statistical Data, 1994, John Wiley and Sons, Chichester.Google Scholar
13. Tarassenko, L., A guide to Neural Computing Applications, 1998, Arnold.Google Scholar
14. Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning, 1989, Addison-Wesley Publishing, Reading, MA.Google Scholar
15. Michalewicz, Z., Genetic Algorithms + Data structures = Evolution Programs, 1996, Springer.Google Scholar
16. Nabney, I.T., NETLAB: Algorithms for Pattern Recognition, 2001, Springer.Google Scholar