Feature extraction for crack detection in rain conditions

Journal of Nondestructive Evaluation

Volumn 26, Issue 2-4, 2007, Pages 57-70

  Kappatos, A Vassilios ^a   Dermatas, S Evangelos ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

Acoustic emission; Crack detection; Features selection; Rain conditions

Indexed keywords

CRACK DETECTOR; CRACK SIGNALS; RAINDROP;

ACOUSTIC EMISSIONS; ADDITIVE NOISE; COMPUTATIONAL COMPLEXITY; FEATURE EXTRACTION; FREQUENCY DOMAIN ANALYSIS; GAUSSIAN NOISE (ELECTRONIC); RADIAL BASIS FUNCTION NETWORKS; SIGNAL TO NOISE RATIO; TIME DOMAIN ANALYSIS; WHITE NOISE;

CRACK DETECTION;

EID: 36048965798     PISSN: 01959298     EISSN: 15734862     Source Type: Journal    
DOI: 10.1007/s10921-007-0020-2     Document Type: Article

References (34)

1. Spanner, J.: Acoustic Emission-Techniques and Applications. Intex (1974)
2. Miller, R., McIntire, P.: Handbook, Vol. 5: Acoustic Emission Testing, Non destructive testing. American Society for Non-Destructive Testing (1987)
3. Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
4. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)
5. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, New York (2001)
6. Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)
7. Berthold, M., Hand, D.: Intelligent Data Analysis, 2nd edn. Springer, New York (2002)
8. Lee, T.: Independent Component Analysis: Theory and Applications. Kluwer Academic, Dordrecht (1998)
9. McCallum, A., Nigam, K., Ungar, L.: Efficient clustering of high-dimensional data sets with application to reference matching. In: 6th ACM SIGKDD - International Conference on Knowledge Discovery and Data Mining, pp. 169-178 (2000) pp. 169-178 (2000)
10. Brusco, M., Cradit, J.: A variable selection heuristic for k-means clustering. Psychometrika 66, 249-270 (2001)
11. Cohen, I., Tian, Q., Zhou, X., Huang, T.: Feature Selection Using Principal Feature Analysis. University of Illinois Press, Urbana-Champaign (2002)
12. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297. University of California Press, Berkeley (1967)
13. Le, D., Satoh, S.: An efficient feature selection method for object detection. In: International Conference on Advances in Pattern Recognition (ICAPR), pp. 461-468 (2005)
14. Krzanowski, W.: Selection of variables to preserve multivariate data structure, using principal component analysis. Appl. Stat. J. R. Stat. Soc. Ser. C 36, 22-33 (1987)
15. Guo, Q., Wu, W., Massart, D., Boucon, C., de Jong, S.: Feature selection in principal component analysis of analytical data. Chemom. Intell. Lab. Syst. 61, 123-132 (2002)
16. Cios, K., Pedrycz, W., Swiniarski, R.: Data Mining Methods in Knowledge Discovery. Kluwer Academic, Boston (1998)
17. Goebel, K., Yan, W.: Feature selection for tool wear diagnosis using soft computing techniques. In: The ASME International Mechanical Engineering Congress and Exhibition, Orlando, pp. 5-10 (2000)
18. Philippidis, T., Nikolaidis, V., Kolaxis, J.: Unsupervised pattern recognition techniques for the prediction of composite failure. J. Acoust. Emiss. 17(1-2), 69-81 (1999)
19. Grabec, I., Sachse, W.: Application of an intelligent signal processing system to AE analysis. J. Acoust. Soc. Am. 85(3), 1226-1235 (1989)
20. Sachse, W., Grabec, I.: Intelligent processing of acoustic emission signals. J. Mater. Eval. 50(7), 826-854 (1992)
21. Barga, R., Friesel, M., Melton, R.: Classification of acoustic emission waves form for nondestructive evaluation using neural network. In: Applications of Artificial Neural Networks, pp. 545-556 (1990)
22. Gongtian, S., Qingru, D., Bangxian, L., Qizhi, L.: The investigation of artificial neural network pattern recognition of acoustic emission signals for pressure vessel. In: 15th WCNDT, pp. 221-224 (2000)
23. Hinton, Y.: Problems associated with statistical pattern recognition of acoustic emission signals in a compact tension fatigue specimen. US Army Research Laboratory Vehicle Technology Directorate Langley Research Center, Hampton, VA (1999)
24. Bhat, M., Majeed, M., Murthy, C.: Characterization of fatigue damage in unidirectional GFRP composites through acoustic emission signal analysis. NDT & E Int. 27(1), 27-32 (1994)
25. Philippidis, T., Nikolaidis, V., Anastassopoulos, A.: Damage characterization of carbon/carbon laminates using neural network techniques on AE signals. NDT & E Int. 31(5), 329-340 (1998)
26. Hugueta, S., Godina, N., Gaertnera, R., Salmonb, L., Villardb, D.: Use of acoustic emission to identify damage modes in glass fibre reinforced polyester. Compos. Sci. Technol. 62, 1433-1444 (2002)
27. Wang, Z., Willett, P., DeAguiar, P., Webster, J.: Neural network detection of grinding burn from acoustic emission. Int. J. Mach. Tools Manuf. 41, 283-309 (2001)
28. Samanta, B.: Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech. Syst. Signal Process. 18, 625-644 (2004)
29. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, New York (1999)
30. Kappatos, V., Dermatas, E.: Classification of acoustic emission and drop signals using SVM, MLP and RBF networks. In: 5th National NDT Conference of the Hellenic Society of Nondestructive Testing (2005)
31. Kappatos, V., Dermatas, E.: Neural network classification of acoustic emission and drop signals. In: 7th International Workshop on Mathematical Methods in Scattering Theory and Biomedical Engineering (2005)
32. Wang, Z., Willett, P., DeAguiar, P., Webster, J.: Neural network detection of grinding burn from acoustic emission. Int. J. Mach. Tools Manuf. 41, 283-309 (2001)
33. Nielsen, A.: Acoustic Emission Source Based on Pencil Lead Breaking. The Danish Welding Institute Publication (1980)
34. Kappatos, V., Dermatas, E.: Technical reports. http://www.wcl2.ee.upatras.gr/ndt/kappatos/