Ultrasonic guided waves offer the possibility of inspecting large areas of structures from a small number of sensor positions. However, inspection of complex structures is difficult as the reflections from different features overlap. Estimating the number and amplitude of the wave packets in ultrasonic time traces is crucial for the development of a guided wave inspection system, in order to detect and locate damage. Deconvolution has been extensively used in geophysical applications to resolve overlapping echoes in the recorded signals. The main objective of this work was to evaluate the applicability of the deconvolution approach for enhancing the resolution of ultrasonic time traces in structural health monitoring (SHM). Numerical simulations on strongly overlapping signals were carried out to evaluate the performance of the two techniques that have been considered: (i) Wiener filter, (ii) single most likely replacement. It was shown that the relatively narrow bandwidth of the input signals and phase shifts between different reflections limit the benefits obtained from deconvolution and it was concluded that deconvolution is unlikely to be useful for guided wave SHM applications.
Boller, C. ( 2000). Next generation structural health monitoring and its integration into aircraft design. International Journal of Systems Science, 31(11), 1333-1349.
2.
Beral, B. and Speckmann, H. ( 2003). Structural health monitoring (SHM) for aircraft structures. A challenge for system developers and aircraft manufacturers. In: Proceedings of the Fourth International Workshop on Structural Health Monitoring , Stanford, California, USA.
3.
Dalton, R.P. , Cawley, P. and Lowe, M.J.S. (2001). The potential of guided waves for monitoring large areas of metallic aircraft fuselage structure . Journal of Non Destructive Evaluation, 20(1), 29-46.
4.
Dalton, R.P. , Cawley, P. and Lowe, M.J.S. (2001). Propagation of acoustic emission signals in metallic fuselage structure. IEE Proceedings - Science, Measurement & Technology, 148(4), 169-177.
5.
Clarke, T., Simonetti, F., Rokhlin, S. and Cawley, P. ( 2008). Evaluation of the temperature stability of a low frequency A0 mode transducer developed for SHM applications. Review of Progress in Quantitative Nondestructive Evaluation, Thompson, D.O. and Chimenti, D.E. (eds), American Institute of Physics, Vol. 27, pp. 910-917.
6.
Fromme, P., Wilcox, P.D., Lowe, M.J.S. and Cawley, P. ( 2006). On the development and testing of a guided ultrasonic wave array for structural integrity monitoring, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 53(4), 777-785.
7.
Gao, H. and Rose, J.L. ( 2006). Ultrasonic sensor placement optimization in structural health monitoring using evolutionary strategy. Review of Progress in Quantitative Nondestructive Evaluation, 25, 1687-1693.
8.
Konstantinidis, G., Drinkwater, B.W. and Wilcox, P.D. (2006). The long term stability of guided wave structural health monitoring systems. Review of Progress in Quantitative Nondestructive Evaluation, 25, 1702-1709.
9.
Robinson, E.A. and Durrani, T.S. ( 1986). Geophysical Signal Processing, Prentice-Hall , Upper Saddle River, NJ, USA.
10.
Gorinevsky, D. ( 2006). Spatio-temporal filter for structural health monitoring . In: Proceedings of the 2006 American Control Conference Minneapolis, Minnesota, USA, pp. 3743-3748.
11.
Silva, M.T. ( 1987). Deconvolution, Handbook of Digital Signal Processing: Engineering Applications, Elliott, D.F. (ed.), Academic Press, San Diego.
12.
Hayes, M.H. ( 1996). Statistical Digital Signal Processing and Modeling, John Wiley & Sons, New York, NY, USA.
13.
Wiener, N. ( 1949). Extrapolation, Interpolation and Smoothing of Stationary Time Series, John Wiley & Sons, New York, NY, USA.
14.
Yilmaz, O. and Doherty, S.M. ( 1987). Seismic Data Processing, Society of Exploration Geophysicists, Tulsa, OK, USA.
15.
Levinson, N. (1947). The Wiener RMS error criterion in filter design and prediction. Journal of Mathematical Physics , 25, 261-278.
16.
Gaydecki, P. (2001). A versatile real-time deconvolution DSP system implemented using a time domain inverse filter. Measurement Science and Technology, 12(1), 82-88.
17.
Neal, S.P., Speckman, P.L. and Enright, M.A. ( 1993). Flaw signature estimation in ultrasonic nondestructive evaluation using the Wiener filter with limited prior information. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 40(4), 347-353.
18.
Honarvar, F. , Sheikhzadeh, H., Moles, M. and Sinclair, A.N. (2004). Improving the time-resolution and signal-to-noise ratio of ultrasonic NDE signals. Ultrasonics , 41(9), 755-763.
19.
Hillery, A.D. and Chin, R.T. (1991). Iterative Wiener filters for image restoration. IEEE Transactions on Signal Processing , 39(8), 1892-1899.
20.
Alleyne, D.N. and Cawley, P. (1992). Optimization of Lamb wave inspection techniques. NDT & International, 25(1), 11-22.
21.
Levy, S. and Oldenburg, D.W. ( 1982). The deconvolution of phase-shifted wavelets. Geophysics, 47(9), 1285-1294.
22.
Ulrych, T.J. and Walker, C. (1982). Analytic minimum entropy deconvolution. Geophysics, 47(9), 1295-1302.
23.
Torvik, P. ( 1967). Reflection and wave trains in semi-infinite plates. Journal of the Acoustical Society of America, 41, 346-353.
24.
Lowe, M.J.S. , Cawley, P., Kao, J.Y. and Diligent, O. (2002). The low frequency reflection characteristics of the fundamental antisymmetric Lamb wave a0 from a rectangular notch in a plate. Journal of the Acoustical Society of America , 112(6), 2612-2622.
25.
Choy, G.L. and Richards, P.G. ( 1975). Pulse distortion and Hilbert transformation in multiply reflected and refracted body waves. Bulletin of the Seismological Society of America, 65(1), 55-70.
26.
Bracewell, R.N. ( 1986). The Fourier Transform and its Applications, 2nd edn, New York: McGraw-Hill Book Co.
27.
Mendel, J.M. ( 1983). Optimal Seismic Deconvolution: An Estimation Based Approach , New York: Academic Press.
28.
Kormylo, J. and Mendel, J.M. ( 1983). Maximum-likelihood seismic deconvolution. IEEE Transactions on Geoscience and Remote Sensing, GE-21(1), 72-82.
29.
Mendel, J.M. ( 1977). White-noise estimators for seismic data processing in oil exploration. IEEE Transactions on Automatic Control, AC-22(5), 694-706.
30.
Mendel, J.M. ( 1990). Maximum-Likelihood Deconvolution: A Journey into Model-Based Signal Processing, Springer-Verlag, New York, NY, USA.
31.
Champagnat, F., Idier, J. and Demoment, G. (1993). Deconvolution of sparse spike trains accounting for wavelet phase shifts and colored noise International Conference on Acoustics Speech, . Signal Processing, ICASS-P93 , 33, 452-455.
32.
Sin, S.K. and Chen, C.H. (1992). A comparison of deconvolution techniques for the ultrasonic nondestructive testing evaluation of material . IEEE Transactions on Image Processing, 1(1), 3-10.
33.
Croxford, A.J. , Wilcox P.D., Drinkwater B.W. and Konstantinidis G. (2007). Strategies for guided-wave structural health monitoring. Proceedings of the Royal Society A, 463, 2961-2981.
