Impact localization with a weighted spectral cross correlation method

The impact localization is a typical inverse problem to identify the damage source of structures. To overcome the positioning problems of the low resolution caused by sparse reference points, a weighted spectral cross-correlation (WSCC) method is proposed. First, a mathematical model with a series of Fiber Bragg Grating (FBG) sensors is built to generate a database of transient frequency responses of each reference point when random shock happens. Second, to identify the spectrum difference between the impact point and the reference point, a cross correlation model is built by mean and normalization operations on the frequency domain. Third, to figure out the impact point among the adjacent reference points, bicubic spline interpolation is realized by subdividing the grid of the WSCC. To validate the accuracy of our suggested method, an impact localization system is designed with a multi FBG sensor network and a composite laminate. The experimental result shows that the error of our suggested method is almost ten times lower than that of other traditional methods. The weighted spectral cross-correlation has advantages in the field of structural health monitoring. (c) 2022 Elsevier Masson SAS. All rights reserved.

Automated summary

This study proposes a weighted spectral cross-correlation (WSCC) method for impact localization in structures, overcoming the positioning problems caused by sparse reference points. The method uses a mathematical model with Fiber Bragg Grating (FBG) sensors to generate a database of frequency responses, and employs cross correlation and bicubic spline interpolation to identify the impact point. Experimental results demonstrate that the proposed method has significantly higher accuracy compared to traditional methods.