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Adaptive sparse reconstruction of damage localization via Lamb waves for structure health monitoring

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Abstract

The application of sparse reconstruction method for damage localization via ultrasonic Lamb waves in structure health monitoring is studied theoretically and experimentally in this paper. In this method, the oblique probes are used to detect damages in thin plate structures by recording all pairwise signals. By using the baseline signals of nondestructive structure and the sparsity of structural damages, a dictionary matrix is constructed through scattering signals from each grid of the detected area. The possible location of damage can be represented by atoms in the dictionary. In order to reduce the effect of noise and unknown sparsity, an adaptive BPDN algorithm is proposed for damage imaging. It combines greedy algorithm and convex optimization algorithm by firstly estimating a sparsity value as the initial iteration step to choose atoms that may contain damages and then renewing the dictionary. The ultrasonic Lamb wave image of the detected plate structure can be obtained by sparsely reconstructing the signals of the new dictionary, and the damages can be located in the image. The results of simulation and experiment manifested the effectiveness of the proposed method.

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References

  1. Li F, Liu Z, Sun X et al (2015) Propagation of guided waves in pressure vessel. Wave Motion 52(4):216–228

    Article  Google Scholar 

  2. Giurgiutiu V (2005) Tuned Lamb wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring. J Intell Mater Syst Struct 16(4):291–305

    Article  Google Scholar 

  3. Santoni GB, Yu L, Xu B et al (2007) Lamb wave-mode tuning of piezoelectric wafer active sensors for structural health monitoring. J Vib Acoust 129(6):752–762

    Article  Google Scholar 

  4. Zhang H, Sun X, Cao Y et al (2010) Structural damage imaging based on time-reversal theory for focusing of Lamb waves. Acta Phys Sin 59(10):7111–7119

    Google Scholar 

  5. Kudela P, Radzienski M, Ostachowicz W (2018) Wave propagation modeling in composites reinforced by randomly oriented fibers. J Sound Vib 414:110–125

    Article  Google Scholar 

  6. Kudela P, Radzienski M, Ostachowicz W (2018) Structural health monitoring system based on a concept of Lamb wave focusing by the piezoelectric array. Mech Syst Signal Process 108:21–32

    Article  Google Scholar 

  7. Shih C-C, Qian X, Ma T et al (2018) Quantitative assessment of thin-layer tissue viscoelastic properties using ultrasonic micro-elastography with Lamb wave model. IEEE Trans Med Imaging 37(8):1887–1898

    Article  Google Scholar 

  8. Perelli A, De Marchi L, Marzani A et al (2014) Frequency warped cross-wavelet multi-resolution analysis of guided waves for impact localization. Signal Process 96(5):51–56

    Article  Google Scholar 

  9. Liu Z, Xue Y, He C (2014) experimental study on defect imaging based on single Lamb wave mode in plate-like structures. Eng Mech 31(4):232–238

    Google Scholar 

  10. Xu Y, Yuan S, Peng G (2004) Study on two-dimensional damage location in structure based on active Lamb wave detection technique. Acta Aeronaut Astronaut Sin 25(5):446–479

    Google Scholar 

  11. Yu Y, Zhang H, Feng G et al (2013) Matching pursuit time-frequency analysis of Lamb wave detection signals. Acta Acust 38(5):576–582

    Google Scholar 

  12. Fenza AD, Sorrentino A, Vitiello P (2015) Application of artificial neural networks and probability ellipse methods for damage detection using Lamb waves. Compos Struct 133:390–403

    Article  Google Scholar 

  13. Pai PF, Deng H, Sundaresan MJ (2015) Time-frequency characterization of Lamb waves for material evaluation and damage inspection of plates. Mech Syst Signal Process (S0888-3270) 62–63:183–206

    Google Scholar 

  14. L Ge, X Wang, C Jin (2014) Numerical modeling of PZT-induced Lamb wave-based crack detection in plate-like structures. Wave Motion (S 0165-2125) 51(6):867–885

    MathSciNet  Google Scholar 

  15. Liu X, Bo L, Liu Y et al (2018) Location identification of closed crack based on Duffing oscillator transient transition. Mech Syst Signal Process 100:384–397

    Article  Google Scholar 

  16. Howard R, Cegla F (2017) Detectability of corrosion damage with circumferential guided waves in reflection and transmission. NDT & E Int 91:108–119

    Article  Google Scholar 

  17. Li R, Li H, Hu B et al (2017) Groundwall insulation damage localization of large generator stator bar using an active Lamb waves method. IEEE Trans Dielectr Electr Insul 24(3):1860–1869

    Article  Google Scholar 

  18. Li H, Li R, Hu B et al (2016) Application of guided waves and probability imaging approach for insulation damage detection of large generator stator bar. IEEE Trans Dielectr Electr Insul 22(6):3216–3225

    Article  Google Scholar 

  19. Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  20. Candès E, Wakin M (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30

    Article  Google Scholar 

  21. Baraniuk RG (2007) Compressive sensing [Lecture Notes]. IEEE Signal Process Mag 24(4):118–121

    Article  Google Scholar 

  22. Hong JC, Sun KH, Kim YY (2005) The matching pursuit approach based on the modulated Gaussian pulse for efficient guided wave damage inspection. Smart Mater Struct 14(4):548–560

    Article  Google Scholar 

  23. Raghavan A, Cesnik CES (2007) Guided-wave signal processing using chirplet matching pursuits and mode correlation for structural health monitoring. Smart Mater Struct 16(2):355–366

    Article  Google Scholar 

  24. Lu Y, Michaels JE (2008) Numerical implementation of matching pursuit for the analysis of complex ultrasonic signals. IEEE Trans Ultrason Ferroelectr Freq Control 55(1):173–182

    Article  Google Scholar 

  25. Masson P, Demers DL, Quaegebeur N (2010) Chirplet-based imaging using compact piezoelectric array. In: Proceedings of SPIE—the international society for optical engineering

  26. Donoho DL, Tsaig Y (2006) Extensions of compressed sensing. Signal Process 86(3):533–548

    Article  MATH  Google Scholar 

  27. Needell D, Vershynin R (2010) Signal recovery from inaccurate and incomplete measurements via regularized orthogonal matching pursuit. IEEE J Sel Top Signal Process 4(2):310–316

    Article  Google Scholar 

  28. Dai W, Milenkovic O (2009) Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans Inf Theory 55(5):2230–2249

    Article  MathSciNet  MATH  Google Scholar 

  29. Needell D, Tropp JA (2008) CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl Comput Harmonic Anal 26(3):301–321

    Article  MathSciNet  MATH  Google Scholar 

  30. Bi X, Shang Z, Qiang Z et al (2014) Improvement of sparsity adaptive matching pursuit based on variable iteration step. J Syst Simul 26(9):2116–2120

    Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61671285, 11474195, 11674214).

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

    Hanfei Zhang, Yu Lu, Shiwei Ma, Shuhao Cao, Qingwei Xia & Yanyan Liu

  2. Huaiyin Normal University, Huai’an, Jiangsu, China

    Hanfei Zhang

  3. School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Haiyan Zhang

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  1. Hanfei Zhang
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  2. Yu Lu
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  3. Shiwei Ma
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  4. Shuhao Cao
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  5. Qingwei Xia
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  6. Yanyan Liu
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  7. Haiyan Zhang
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Correspondence to Shiwei Ma.

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Zhang, H., Lu, Y., Ma, S. et al. Adaptive sparse reconstruction of damage localization via Lamb waves for structure health monitoring. Computing 101, 679–692 (2019). https://doi.org/10.1007/s00607-018-00694-0

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