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Mechanical systems and signal processing v.103, 2018년, pp.89 - 104   SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

A guided wave dispersion compensation method based on compressed sensing

Xu, Cai-bin (School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China ); Yang, Zhi-bo (School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China ); Chen, Xue-feng (School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China ); Tian, Shao-hua (Collaborative Innovation Center of High-end Manufacturing Equipment, Xi'an Jiaotong University, Xi'an 710049, China ); Xie, Yong (School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China );
  • 초록  

    Abstract The ultrasonic guided wave has emerged as a promising tool for structural health monitoring (SHM) and nondestructive testing (NDT) due to their capability to propagate over long distances with minimal loss and sensitivity to both surface and subsurface defects. The dispersion effect degrades the temporal and spatial resolution of guided waves. A novel ultrasonic guided wave processing method for both single mode and multi-mode guided waves dispersion compensation is proposed in this work based on compressed sensing, in which a dispersion signal dictionary is built by utilizing the dispersion curves of the guided wave modes in order to sparsely decompose the recorded dispersive guided waves. Dispersion-compensated guided waves are obtained by utilizing a non-dispersion signal dictionary and the results of sparse decomposition. Numerical simulations and experiments are implemented to verify the effectiveness of the developed method for both single mode and multi-mode guided waves. Highlights A guided wave dispersion compensation based on compressed sensing method is proposed. The dispersive signal can be compensated to the waveform of the excitation. Both single mode and multi-mode dispersive guided waves can be compensated.


  • 주제어

    Dispersion compensation .   Guided wave .   Compressed sensing .   Sparse representation .   Structural health monitoring.  

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