Robust Localization of Low-Velocity Impacts on Honeycomb Sandwich Panels via FBG Sensor Networks.

Honeycomb sandwich panels are widely used in aerospace, yet they are vulnerable to low-velocity impacts. Implementing effective localization is challenging because, unlike single-layer structures, the multi-layer energy dissipation capabilities of honeycomb core induce rapid stress wave attenuation and reverberations, degrading signal quality. This paper designs a testing platform for low-velocity impact and proposes a template matching method based on wavelet denoising and error outlier weighting. This method is based on 16 FBG sensors uniformly arranged on the panel, dividing the panel into 25 × 25 grids, with five impacts in each grid forming a template library. Similarity matching is performed by calculating the Euclidean distance between the template library and test signals, combined with wavelet denoising and outlier weighting to compute the average localization accuracy. The results show that for a honeycomb panel measuring 500 mm × 500 mm × 20 mm, the basic method yields an average localization accuracy of 21.29 mm. When error outlier weighting is applied, the accuracy improves to 12.36 mm. Finally, by combining outlier weighting with Sym5 wavelet denoising, the average error is further reduced to 8.53 mm. These results demonstrate that the proposed method mitigates the effects of signal instability in honeycomb structures, providing a robust and precise solution for aerospace SHM where traditional methods fall short.