Structural health monitoring using statistical pattern recognition techniques

Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME

Volumn 123, Issue 4, 2001, Pages 706-711

  Sohn, Hoon ^a   Farrar, Charles R ^a   Hunter, Norman F ^a   Worden, Keith ^b  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0001252638     PISSN: 00220434     EISSN: 15289028     Source Type: Journal    
DOI: 10.1115/1.1410933     Document Type: Article

References (20)

1. [1] Doebling, S. W., Farrar, C. R., Prime, M. B., and Shevitz, D. W., 1998, “A Review of Damage Identification Methods That Examine Changes in Dynamic Properties,” Shock Vib. Dig., 30, No. 2, pp. 91-105.
2. Farrar, C. R., Duffey, T. A., Doebling, S. W., Nix, D. A., 2000, “A Statistical Pattern Recognition Paradigm for Vibration-Based Structural Health Monitoring,” Proceedings of the 2nd International Workshop on Structural Health Monitoring, Stanford, CA, pp. 764-773.
3. Sohn, H., Czarnecki, J. J., and Farrar, C. R., 2001a, “Structural Health Monitoring Using Statistical Process Control,” J. Eng. Mech., 126, No. 11, pp. 1356-1363.
4. Sohn, H., and Farrar, C. F., 2001b, “Damage Diagnosis Using Time Series Analysis of Vibration Signals,” Smart Mater. Struct., 10, pp. 446-451.
5. Worden, K., 1997, “Structural Fault Detection Using a Novelty Measure,” J. Sound Vib., 201, pp. 85-101.
6. Worden, K., Manson, G., and Fieller, N. J., 2000, “Damage Detection Using Outlier Analysis,” J. Sound Vib., 229, pp. 647-667.
7. Fugate, M. L., Sohn, H., and Farrar, C. R., 2001, “Vibration-Based Damage Detection Using Statistical Process Control,” Mech. Syst. Signal Process., 15, No. 4, pp. 707-721.
8. Wang, G. and Pran, K., 2000, “Ship Hull Structure Monitoring Using Fiber Optic Sensors,” Proceedings of European COST F3 Conference on System Identification & Structure Health Monitoring, Universidad Politecnica de Madrid, Spain, Vol. 1, pp. 15-17.
9. Johnson, G. A., Pran, K., Sagvolden, G., Farsund, O., Havsgard, G. B., Wang, G., Jensen A. E., and Taby J., 2000, “Surface Effect Ship Vibro-Impact Monitoring With Distributed Arrays of Fiber Bragg Gratings,” Proceedings of the 18th International Modal Analysis, San Antonio, TX, pp. 1406-1411.
10. Sohn, H., Farrar, C. R., Hunter, H. F., and Worden, K., 2001c, “Applying the LANL Statistical Pattern Recognition Paradigm for Structural Health Monitoring to Data From a Surface-Effect Fast Patrol Boat,” Los Alamos National Laboratory Report, LA-13761-MS.
11. Box, G. E., Jenkins, G. M., and Reinsel, G. C., 1994, Time Series Analysis: Forecasting and Control, Prentice-Hall, New Jersey.
12. Brockwell, P. J., and Davis, R. A., 1991, Time Series: Theory and Methods, Springer, New York.
13. Ljung, L., 1987, System Identification: Theory for the User, Prentice Hall, New Jersey.
14. Box, G. E. P., and Andersen, S. L., 1995, “Permutation Theory in the Derivation of Robust Criteria and the Study of Departures From Assumption,” Journal of the Royal Statistical Society, Series B17, pp. 1-26.
15. Miller, R. G., 1997, Beyond ANOVA: Basics of Applied Statistics, Chapman&Hall/CRC, New York.
16. Barnett, V., and Lewis, T., 1994, Outliers in Statistical Data, Third Edition, Wiley, Chichester, UK.
17. Bishop, C. M., 1995, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK.
18. Tarassenko, L., Hayton, P., Cerneaz, Z., and Brady, M., 1995, “Novelty Detection for the Identification of Masses in Mammograms,” Proceedings of 4th IEEE International Conference on Artificial Neural Networks, Cambridge, UK, IEEE Conference Publication No. 409, pp. 442-447.
19. Pomerleau, D. A., 1993, “Input Reconstruction Reliability Estimation,” Advances in Neural Information Processing Systems 5, S. J. Hanson, J. D. Cowan, and C. L. Giles, eds., Morgan Kaufman Publishers, San Mateo, CA.
20. Oppenheim, A. V., and Willsky, S. H., 1996, Signals & Systems, Prentice Hall, New Jersey.