The structure health monitoring (SHM) system with Lamb waves technique is studied numerically and experimentally in this paper. A damage shape recognition algorithm (DSRA) is developed for the hole on aluminum alloy plate specimen. The proposed DSRA is established based on the two arrival time difference method (2/ATDM) and Lagrangian optimization method. This method captures the damage shape by describing the coordinates of the reflection point from the piezoelectric ceramic lead zirconate titanate (PZT) transducers to the damage edge. 2/ATDM provides a way to obtain coordinate points by the arrival time which is determined by Lamb waves. And the coordinate points are optimized by the Lagrangian optimization method. A numerical model is established to simulate the proposed experiment process. Its convergence rate of mesh size and time steps is investigated, and the numerical results are verified by these of experiment. Hence, the shape recognition of damage occurring at an arbitrary position and that of irregular damage are studied by the proposed numerical and experiment methods.
AlleyneDCawleyP (1991) A two-dimensional fourier transform method for the measurement of propagating multimode signals. The Journal of the Acoustical Society of America89(3): 1159–1168.
2.
AndhaleYSMasurkarFYelveN, et al. (2019) Localization of damages in plain and riveted aluminium specimens using lamb waves. International Journal of Acoustics & Vibration24(1): 150–165.
3.
BalvantínABaltazarARodriguezP (2014) Characterization of laser generated lamb wave modes after interaction with a thickness reduction discontinuity using ray tracing theory. Experimental Mechanics54(5): 743–752.
4.
CucAGiurgiutiuVJoshiS, et al. (2007) Structural health monitoring with piezoelectric wafer active sensors for space applications. Aiaa Journal45(12): 2838–2850.
5.
De FenzaASorrentinoAVitielloP (2015) Application of artificial neural networks and probability ellipse methods for damage detection using lamb waves. Composite Structures133: 390–403.
6.
FengBPasadasDJRibeiroAL, et al. (2019) Locating defects in anisotropic cfrp plates using tof-based probability matrix and neural networks. IEEE Transactions on Instrumentation and Measurement68(5): 1252–1260.
7.
GuyPJayetYGoujonL (2003) Guided waves interaction with complex delaminations. application to damage detection in composite structures. In: Proceedings of Spie the international society for optical engineering, San Diego, California, United States, 1 August, pp.5047.
8.
HameedMSLiZChenJ, et al. (2019) Lamb-wave-based multistage damage detection method using an active pzt sensor network for large structures. Sensors19(9): 2010.
9.
HongJCSunKHKimYY (2005) Adaptive short-time fourier transform for guided-wave analysis. Transactions of the Korean Society for Noise & Vibration Engineering15(3): 266–271.
10.
JiangMWangG (2003) Convergence of the simultaneous algebraic reconstruction technique (sart). IEEE Transactions on Image Processing12(8): 957–961.
11.
KannusamyMKapuriaSSasmalS (2020) Accurate baseline-free damage localization in plates using refined lamb wave time-reversal method. Smart Materials and Structures29(5): 055044.
12.
KehlenbachMHanselkaH (2003) Automated structural integrity monitoring based on broadband lamb wave excitation and matched filtering. In: Aiaa/asme/asce/ahs/asc structures, structural dynamics, & materials conference, Norfolk, Virginia, 7–10 April.
13.
MalyarenkoEVHindersMK (2000) Fan beam and double crosshole lamb wave tomography for mapping flaws in aging aircraft structures. The Journal of the Acoustical Society of America108(4): 1631–1639.
14.
MasurkarFAYelveNP (2017) Optimizing location of damage within an enclosed area defined by an algorithm based on the lamb wave response data. Applied Acoustics120: 98–110.
15.
MijarARAroraJS (2004) An augmented lagrangian optimization method for contact analysis problems, 2: numerical evaluation. Structural & Multidisciplinary Optimization28(2): 113–126.
16.
MoserFJacobsLJQuJ (1999) Modeling elastic wave propagation in waveguides with the finite element method. NDT & E International32(4): 225–234.
17.
ParkSYunCBRohY, et al. (2006) Pzt-based active damage detection techniques for steel bridge components. Smart Materials & Structures15(4): 957–966.
18.
QiuLYanXLinX, et al. (2019) Multiphysics simulation method of lamb wavepropagation with piezoelectric transducers under load condition. Chinese Journal of Aeronautics32(5): 1071–1086.
19.
RajagopalanJBalasubramaniamKKrishnamurthyC (2006a) A phase reconstruction algorithm for lamb wave based structural health monitoring of anisotropic multilayered composite plates. The Journal of the Acoustical Society of America119(2): 872–878.
20.
RajagopalanJBalasubramaniamKKrishnamurthyCV (2006b) A single transmitter multi-receiver (stmr) pzt array for guided ultrasonic wave based structural health monitoring of large isotropic plate structures. Smart Material Structures15(5): 1190–1196.
21.
RealRVargasJM (1996) The probabilistic basis of Jaccard’s index of similarity. Systematic biology45(3): 380–385.
22.
RioulOVetterliM (1991) Wavelets and signal processing. IEEE Signal Processing Magazine8(4): 14–38.
23.
SinhaSRouthPSAnnoPD, et al (2005) Spectral decomposition of seismic data with continuous wavelet transform. Geophysics70(6): P19–P25.
24.
SuZYeL (2009) Identification of Damage Using Lamb Waves: From Fundamentals to Applications, vol. 48. Berlin, Germany: Springer Science & Business Media.
25.
TsePMasurkarFYelveNP (2020) Estimation of remaining useful life of fatigued plate specimens using lamb wave-based nonlinearity parameters. Structural Control and Health Monitoring27(4): e2486.
26.
YuFTSLuG (1994) Short-time fourier transform and wavelet transform with fourier-domain processing. Applied Optics33(23): 5262–5270.
27.
YuLGiurgiutiuV (2008) In situ 2-d piezoelectric wafer active sensors arrays for guided wave damage detection. Ultrasonics48(2): 117–134.
28.
ZhengLYiJlChenJl, et al. (2019) Ultrasonic lamb waves applied in nondestractive damage evaluation. In: 2019 Symposium on Piezoelectrcity, Acoustic Waves and Device Applications (SPAWDA), Harbin, China, 11–14 January, pp.1–4. New York, NY: IEEE.
