Abstract
Pulsed eddy current (PEC) technique has been applied as a viable method to detect hidden discontinuities in metallic structures. Conventionally, selected time-domain features are employed to characterize the PEC data, such as peak value, lift-off point of intersection, rising point, crossing time, and differential time to peak. The research presented in this paper continues the effort in a previous study on detecting the radial cracks starting from the fastener hole in second layer of a two-layer mock-up aircraft structure. A large diameter excitation coil with ferrite core is used to induce a strong pulse, and the magnetic field generated by eddy current is detected by Hall sensors. Instead of analyzing the limited time-domain features, we propose using machine learning methods to interpret the raw data without feature extraction. Thus, the second-layer discontinuities can be characterized presumably with all the information contained in a waveform. An automated detection framework is proposed in this paper and the experimental results demonstrate the effectiveness of the proposed method.
This is a preview of subscription content, access via your institution.






References
Sophian, A., Tian, G.Y., Taylor, D., Rudlin, J.: Electromagnetic and eddy current NDT: a review. INSIGHT 43(5), 302–306 (2001)
Sophian, A., Tian, G., Fan, M.: Pulsed eddy current non-destructive testing and evaluation: a review. Chin. J. Mech. Eng. 30(3), 500–514 (2017)
Kriezis, E., Tsiboukis, T., Panas, S.M., Tegopoulos, J.: Eddy currents: theory and applications. Proc. IEEE 80(10), 1559–1589 (1992)
Tian, G.Y., Zhao, Z.X., Baines, R.W.: The research of inhomogeneity in eddy current sensors. Sens. Actuators A Physi. 69(2), 148–151 (1998)
Park, D.-G., Sekar Angani, C., Rao, B.P.C., Vértesy, G., Lee, D.-H., Kim, K.-H.: Detection of the subsurface cracks in a stainless steel plate using pulsed eddy current. J. Nondestruct. Eval. 32(4), 350–353 (2013)
Angani, C.S., Park, D.G., Kim, C.G., Leela, P., Kollu, P., Cheong, Y.M.: The pulsed eddy current differential probe to detect a thickness variation in an insulated stainless steel. J. Nondestruct. Eval. 29(4), 248–252 (2010)
Ghoni, R., Dollah, M., Sulaiman, A., Mamat Ibrahim., F.: Defect characterization based on eddy current technique: technical review. Adv. Mech. Eng. 6, 182496 (2014)
Chen, T., Tian, G.Y., Sophian, A., Que, P.W.: Feature extraction and selection for defect classification of pulsed eddy current NDT. NDT & E Int. 41(6), 467–476 (2008)
Safizadeh, M.S., Forsyth, D.S., Liu, Z., Lepine, B.A., Liao, M.: Pulsed eddy current inspections of aircraft structures in support of holistic damage tolerance. In: Proceedings of the Aerospace Manufacturing Technology Conference & Exposition. SAE International (2003)
Xu, Z., Wu, X., Li, J., Kang, Y.: Assessment of wall thinning in insulated ferromagnetic pipes using the time-to-peak of differential pulsed eddy-current testing signals. NDT & E Int. 51, 24–29 (2012)
He, Y., Luo, F., Pan, M., Weng, F., Xiangchao, H., Gao, J., Liu, Bo: Pulsed eddy current technique for defect detection in aircraft riveted structures. NDT & E Int. 43(2), 176–181 (2010)
Li, Y., Chen, Z., Qi, Y.: Generalized analytical expressions of liftoff intersection in pec and a liftoff-intersection-based fast inverse model. IEEE Trans. Magn. 47(10), 2931–2934 (2011)
Tian, G.Y., Li, Y., Mandache, C.: Study of lift-off invariance for pulsed eddy-current signals. IEEE Trans. Magn. 45(1), 184–191 (2009)
Tian, G.Y., Sophian, A.: Defect classification using a new feature for pulsed eddy current sensors. NDT & E Int. 38(1), 77–82 (2005)
He, Y., Luo, F., Pan, M., Xiangchao, H., Gao, J., Liu, B.: Defect classification based on rectangular pulsed eddy current sensor in different directions. Sens. Actuators A Phys. 157(1), 26–31 (2010)
Stott, C.A., Underhill, P.R., Babbar, V.K.: Pulsed eddy current detection of cracks in multilayer aluminum lap joints. IEEE Sens. J. 15(2), 956–962 (2015)
Butt, D.M., Underhill, P.R., Krause, T.W.: Examination of pulsed eddy current for inspection of second layer aircraft wing lap-joint structures using outlier detection methods. CINDE J. 37(5), 6–10 (2016)
Butt, D., Underhill, R., Krause,T.W.: Pulsed eddy current detection of second layer cracks at ferrous fasteners in aircraft lap-joint structures. In: Proceedings of the 19th World Conference on Non-Destructive Testing, pp. 1–8, Munich, Germany (2016)
Pan, M., He, Y., Tian, G., Chen, D., Luo, F.: PEC frequency band selection for locating defects in two-layer aircraft structures with air gap variations. IEEE Trans. Instrum. Meas. 62(10), 2849–2856 (2013)
Safizadeh, M.S., Lepine, B.A., Forsyth, D.S., Fahr, A.: Time–frequency analysis of pulsed eddy current signals. J. Nondestruct. Eval. 20(2), 73–86 (2001)
Hosseini, S.M.S.: Detection of hidden corrosion by pulsed eddy current using time frequency analysis. Ph.D. Thesis, Universite de Montreal, Montreal, Quebec, Canada (2012)
He, Y., Pan, M., Luo, F., Chen, D., Xiangchao, H.: Support vector machine and optimised feature extraction in integrated eddy current instrument. Measurement 46(1), 764–774 (2013)
Liu, Z., Forsyth, D.S., Lepine, B.A., Hammad, I., Farahbakhsh, B.: Investigations on classifying pulsed eddy current signals with a neural network. INSIGHT 45(9), 608–614 (2003)
Mandache, C., Whalen, P.: A gradual approach for the detection of second layer cracks using the pulsed eddy current technique. In: Proceedings of the Aircraft Airworthiness&Sustainment Conference (2012)
Zheng, B., Yoon, S.W., Lam, S.S.: Breast cancer diagnosis based on feature extraction using a hybrid of k-means and support vector machine algorithms. Expert Syst. Appl. 41(4), 1476–1482 (2014)
Tehrany, M.S., Pradhan, B., Jebur, M.N.: Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J. Hydrol. (Amsterdam) 512, 332–343 (2014)
John, V., Mita, S., Liu, Z., Qi, B.: Pedestrian detection in thermal images using adaptive fuzzy c-means clustering and convolutional neural networks. In: Proceedings of the 2015 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 246–249. IEEE (2015)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2013)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Ho, T.K.: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. ICML 96, 148–156 (1996)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Oshiro, T.M., Perez, P.S., Baranauskas, J.A.: How many trees in a random forest? In: Proceedings of the MLDM, pp. 154–168. Springer (2012)
Acknowledgements
Mr. Marc Genest at National Research Council of Canada is acknowledged for his valuable comments and feedback, which help improve the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, Y., Liu, S., Liu, H. et al. Pulsed Eddy Current Data Analysis for the Characterization of the Second-Layer Discontinuities. J Nondestruct Eval 38, 7 (2019). https://doi.org/10.1007/s10921-018-0545-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10921-018-0545-6
Keywords
- Pulse eddy current
- Lift-off intersection
- Second-layer crack
- Machine learning
- Classification