An evidence-based approach to damage location on an aircraft structure

Mechanical Systems and Signal Processing

Volumn 23, Issue 6, 2009, Pages 1792-1804

  Worden, K ^a   Manson, G ^a   Denoeux T ^b  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

Author keywords

Damage location; Dempster Schafer theory; Structural health monitoring

Indexed keywords

AIRCRAFT STRUCTURES; AIRCRAFT WINGS; DAMAGE LOCATION; DEMPSTER - SHAFER; DEMPSTER-SCHAFER THEORY; DEMPSTER-SHAFER THEORIES; INDIVIDUAL CLASSIFIERS; MLP CLASSIFIERS; MULTI-LAYER PERCEPTRON; PROBABILISTIC CLASSIFIERS;

AIRCRAFT; AIRCRAFT MANUFACTURE; AIRFRAMES; CLASSIFIERS; DAMAGE DETECTION; FORMAL LOGIC; LEARNING SYSTEMS; NETWORK LAYERS; NEURAL NETWORKS; STRUCTURES (BUILT OBJECTS);

STRUCTURAL HEALTH MONITORING;

EID: 67349193685     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2008.11.003     Document Type: Article

References (21)

1. Hayton P., Utete S., King D., King S., Anuzis P., and Tarassenko L. Static and dynamic novelty detection methods for jet engine health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 (2007) 514-593
2. Worden K., and Manson G. The application of machine learning to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365 (2007) 515-537
3. Bishop C.M. Neural Networks for Pattern Recognition (1998), Oxford University Press, Oxford
4. Shafer G. A Mathematical Theory of Evidence (1976), Princeton University Press, Princeton, NJ
5. R.A. Osegueda, S.R. Seelam, B. Mulupuru, V. Kreinovich, Statistical and Dempster-Shafer techniques in testing structural integrity of aerospace structures, In: NDE for Health Monitoring and Diagnostics, SPIE, San Diego, CA, Paper no. 5047-18, 2003.
6. Yang B.-S., and Kim K.J. Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing 20 (2006) 403-420
7. Denœux T. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics 30 (2000) 131-150
8. Dempster A.P. Upper and lower probabilities induced by a multi-valued mapping. Annals of Mathematical Statistics 38 (1967) 325-339
9. Smets Ph. The combination of evidence in the transferable belief model. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 447-458
10. Thomopoulos S.C.A. Theories in distributed decision fusion: comparison and generalisation. Sensor Fusion III: 3D Perception and Recognition. Proceedings of SPIE 1383 (1990)
11. Yen J. A reasoning model based on an extended Dempster-Shafer theory. Proceedings of the 5th AAAI-86 National Conference on Artificial Intelligence (1986) 125-131
12. Denœux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics 25 (1995) 804-813
13. Denœux T. Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition 30 (1997) 1095-1107
14. Worden K., Manson G., and Allman D.J. Experimental validation of a structural health monitoring methodology I: novelty detection on a laboratory structure. Journal of Sound and Vibration 259 (2003) 323-343
15. Manson G., Worden K., and Allman D.J. Experimental validation of a structural health monitoring methodology II: novelty detection on an aircraft wing. Journal of Sound and Vibration 259 (2003) 345-363
16. Manson G., Worden K., and Allman D.J. Experimental validation of a structural health monitoring methodology III: damage location on an aircraft wing. Journal of Sound and Vibration 259 (2003) 365-385
17. Worden K., Manson G., Hilson G., and Pierce S.G. Genetic optimisation of a neural damage locator. Journal of Sound and Vibration 309 (2007) 529-544
18. Worden K., Manson G., and Fieller N.R.J. Damage detection using outlier analysis. Journal of Sound and Vibration 229 (2000) 647-667
19. Tarassenko L. A Guide to Neural Computing Applications (1998), Arnold, Paris
20. G.O. Chandroth, Diagnostic classifier ensembles: enforcing diversity for reliability in the combination, Ph.D. Thesis, Department of Computer Science, University of Sheffield, 2000.
21. Richard M.D., and Lippmann R.P. Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation 3 (1991) 461-483