Damage detection in pipes under changing environmental conditions using embedded piezoelectric transducers and pattern recognition techniques

Journal of Pipeline Systems Engineering and Practice

Volumn 4, Issue 1, 2013, Pages 17-23

  Ying, Yujie ^a   Garrett, James H ^a   Harley, Joel ^a   Oppenheim, Irving J ^a   Shi, Jun ^a   Soibelman, Lucio ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Damage detection; Environmental and operational variations; Machine learning; Nondestructive testing; Pattern recognition; Piezoelectric transducers; Pipeline; Structural health monitoring; Support vector machines; Ultrasonics

Indexed keywords

DAMAGE DETECTION; NONDESTRUCTIVE EXAMINATION; PATTERN RECOGNITION; PIEZOELECTRIC TRANSDUCERS; PIEZOELECTRICITY; PIPELINES; STRUCTURAL HEALTH MONITORING; ULTRASONIC TESTING; ULTRASONICS;

CONTINUOUS MONITORING; ENVIRONMENTAL AND OPERATIONAL VARIATION; ENVIRONMENTAL CONDITIONS; IN-PIPE; MACHINE-LEARNING; MONITORING TECHNIQUES; MULTI-MODAL; OPERATIONAL CONDITIONS; PATTERN RECOGNITIONS TECHNIQUES; SUPPORT VECTORS MACHINE;

SUPPORT VECTOR MACHINES;

EID: 84879563720     PISSN: 19491190     EISSN: 19491204     Source Type: Journal    
DOI: 10.1061/(ASCE)PS.1949-1204.0000106     Document Type: Article

References (12)

1. Burges, C. J. (1998). "A tutorial on support vector machines for pattern recognition." Data Min. Knowl. Discov., 2(2), 121-167.
2. Chang, C., and Lin, C. (2001). "LIBSVM: A library for support vector machines." Software available at (Nov. 8, 2012).
3. Cortes, C., and Vapnik, V. (1995). "Support-vector networks." Mach. Learn., 3(20), 273-297.
4. Freund, Y., and Schapire, R. E. (1997). "A decision-theoretic generalization of on-line learning and an application to boosting." J. Comput. Syst. Sci., 55(1), 119-139.
5. Lowe, M. J. S., Alleyne, D. N., and Cawley, P. (1998). "Defect detection in pipes using guided waves." Ultrasonics, 36(1-5), 147-154.
6. Lu, Y., and Michaels, J. E. (2009). "Feature extraction and sensor fusion for ultrasonic structural health monitoring under changing environmental conditions." IEEE Sensor. J., 9(11), 1462-1471.
7. Mallat, S. G. (1999). A wavelet tour of signal processing, Academic Press, New York.
8. Rizzo, P., Bartoli, I., Marzani, A., and Di Scalea, F. L. (2005). "Defect classification in pipes by neural networks using multiple guided ultrasonic wave features extracted after wavelet processing." J. Pressure Vessel Technol., 127(3), 294-303.
9. Sohn, H., Farrar, C. R., Hunter, N. F., and Worden, K. (2001). "Structural health monitoring using statistical pattern recognition techniques." J. Dyn. Syst. Meas. Contr., 123(4), 706-711.
10. Worden, K., and Manson, G. (2007). "The application of machine learning to structural health monitoring." Philos. Trans. R. Soc., A, 365(1851), 515-537.
11. Ying, Y., et al. (2010). "Time reversal for damage detection in pipes." Proc. SPIE, 7647, 76473S.1-12.
12. Ying, Y., et al. (2011). "Applications of machine learning in pipeline monitoring." Proc., 2011 ASCE Int. Workshop on Computing in Civil Engineering, Miami, FL, ASCE, Reston, VA, 242-249.