The application of machine learning to structural health monitoring

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Volumn 365, Issue 1851, 2007, Pages 515-537

  Worden, Keith ^a   Manson, Graeme ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Machine learning; Neural networks; Pattern recognition; Statistical learning theory; Structural health monitoring; Support vector machines

Indexed keywords

EID: 33846956777     PISSN: 1364503X     EISSN: None     Source Type: Journal    
DOI: 10.1098/rsta.2006.1938     Document Type: Article

References (30)

1. Barnett, L. & Lewis, T. 1994 Outliers in statistical data, 3rd edn. Chichester, UK: Wiley.
2. Bedworth, M. & O'Brien, J. 2000 The omnibus model: a new model of data fusion. IEEE Aerospace and Electronics Systems Magazine 15, 30-36.
3. Bennett, K. P. 1999 Combining support vector and mathematical programming methods for classification. In Advances in Kernel methods - support vector learning (ed. B. Schoelkopf, C. J. C. Burgess & A. J. Smola), pp. 307-326. Cambridge, MA: MIT Press.
4. Bishop, C. M. 1998 Neural networks for pattern recognition. Oxford, UK: Oxford University Press.
5. Burges, C. J. C. 1998 A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 1-47. (doi:10.1023/A: 1009715923555)
6. Cherkassky, V. & Mulier, F. 1998 Learning from data, concepts, theory and methods. New York, NY: Wiley.
7. Cristianini, N. & Shawe-Taylor, J. 2000 An introduction to support vector machines and other Kernel-based learning methods. Cambridge, UK: Cambridge University Press.
8. Doebling, S. W., Farrar, C. R., Prime, M. B. & Shevitz, D. W. 1996 Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Los Alamos National Laboratories Report LA-13070-MS.
9. Joachims, T. 1999 Making large-scale support vector machine learning practical. In Advances in Kerne methods - support vector learning (ed. B. Schoelkopf, C. J. C. Burgess & A. J. Smola), pp. 169-184. Cambridge, MA: MIT Press.
10. Lowe, D. 2000 Feature extraction, data visualisation, classification and fusion for damage assessment. Oral Presentation at EPSRC SIDANET meeting, Derby, UK.
11. Manevitz, L. M. & Yousef, M. 2001 One-class SVMs for document classification. J. Mach. Learning Res. 2, 139-154. (doi:10.1162/15324430260185574)
12. st Eur. Workshop on Structural Health Monitoring, Paris, France. pp. 845-852.
13. Manson, G., Worden, K. & Allman, D. J. 2003a Experimental validation of structural health monitoring methodology II: novelty detection on a aircraft wing. J. Sound Vib. 259, 345-363. (doi:10.1006/jsvi.2002.5167)
14. Manson, G., Worden, K. & Allman, D. J. 2003b Experimental validation of structural health monitoring methodology III: damage location on a aircraft wing. J. Sound Vib. 259, 365-385. (doi:10.1006/jsvi.2002.5169)
15. th Int. Conf. Artificial Neural Networks, Cambridge, UK.
16. Rytter, A. 1993 Vibration based inspection of civil engineering structures. Ph.D. thesis, Department of Building Technology and Structural Engineering, University of Aalborg, Denmark.
17. Silverman, B. W. 1986 Density estimation for statistics and data analysis Monographs on statistics and applied probability, vol. 26. Boca Raton, FL: Chapman and Hall.
18. Sohn, H., Dzwonczyk, M., Straser, E. G., Kiremidjian, A. S., Law, K. H. & Meng, T. 1999 An experimental study of temperature effects on modal parameters of the Alamosa Canyon Bridge. Earthq. Eng. Struct. Dyn. 28, 879-897. (doi:10.1002/(SICI)1096-9845(199908)28:8〈879::AID- EQE845〉3.0.CO;2-V)
19. Sohn, H., Farrar, C. R., Hunter, N. F. & Worden, K. 2001 Structural health monitoring using statistical pattern recognition techniques. ASME J. Dyn. Meas. Syst. Control 123, 706-711. (doi:10.1115/1.1410933)
20. Sohn, H., Farrar, C. R., Hemez, F. M., Shunk, D. D., Stinemates, D. W., Nadler, B. & Czarnecki, J. J. 2003 A review of structural health monitoring literature: 1996-2001. Los Alamos National Laboratories Report LA-13976-MS.
21. Tarassenko, L. 1998 A guide to neural computing applications. New York, NY: Hodder Arnold.
22. th IEE Int. Conf. Artificial Neural Networks, Cambridge, UK, IEE Conference Publication, 409, pp. 442-447.
23. Vapnik, V. 1995 The nature of statistical learning theory. Berlin, Germany: Springer.
24. Vapnik, V. 1998 Statistical learning theory. New York, NY: Wiley.
25. Worden, K. 2003 Cost action F3 on structural dynamics: benchmarks for working group 2-structural health monitoring. Mech. Syst. Signal Process. 17, 73-75. (doi:10.1006/mssp.2002.1541)
26. Worden, K. & Staszewski, W. J. 2000 Impact location and quantification on a composite panel using neural networks and genetic algorithms. Strain 36, 61-70.
27. Worden, K. & Staszewski, W. J. 2003 Data fusion: the role of signal processing for smart systems and structures. In Smart technologies (ed. K. Worden, W. A. Bullough & J. Haywood), pp. 71-100. Singapore: World Scientific.
28. Worden, K., Manson, G. & Fieller, N. R. 1999 Damage detection using outlier analysis. J. Sound Vib. 229, 647-667. (doi:10.1006/jsvi. 1999.2514)
29. th Int. Modal Analysis Conf., Orlando, Florida, pp. 1234-1239.
30. Worden, K., Manson, G. & Allman, D. J. 2003 Experimental validation of structural health monitoring methodology I: novelty detection on a laboratory structure. J. Sound Vib. 259, 323-343. (doi:10.1006/jsvi.2002. 5168)