Lamb waves defects imaging research based on multi-parameter compensation and pixel optimization

Abstract

Ultrasonic guided waves detecting technology has promising application prospects in structural health monitoring. In order to detect defects in the aluminum sheet, a kind of defect localization imaging algorithm, combining multi-parameter compensation and pixel partitioning optimization is proposed in this paper. Based on the analysis of imaging principles, the waveform signal of time domain was mapped onto the wavenumber domain through a backward propagation compensation, so dispersion parameters can be compensated. Reference signal compensation can be achieved by the baseline signal differential method from wavenumber domain, which overcame influences of environmental changes. During the imaging process, a reasonable threshold was used for pixel partitioning and optimization to improve image quality. Experimental results demonstrated that positioning error about the algorithm is small, defects imaging of sheet is clear and intuitive, this optimization and compensation of guided-wave defects imaging can be used in structural health monitoring.

Lamb wave based damage imaging using an adaptive Capon method

“…As can be seen from figure 1, the pixel value decreases as f decreases when the inner product between e and v is fixed except the values of 0 and 1, which means that the pixel value is not independent of the value of f. A large value of f should be adopted when the inner product between e and v is large to obtain a large pixel value, while a small value of f should be adopted when the inner product is small to suppress background noise. It should be noted that, f is independent of e and v, and the change of f does not affect the inner product between e and v. In the conventional Capon method, the value of f is determined by experience and f = 0.1 is used as a reference in many related researches [24,25,28]. The imaging quality can be improved if the appropriate value of f is utilized adaptively at each imaging point.…”

“…where ρ i,h is the Spearman correlation coefficient, i and h represent the ith and hth signals respectively. As mentioned above, the constant coefficient 0.1 in equation ( 16) is set according to the reference value in the paper [23,24,28].…”