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

## TL;DR

A new method using FBG sensors and signal processing improves the accuracy of detecting low-velocity impacts on aerospace honeycomb panels.

## Contribution

Combining wavelet denoising and error outlier weighting in template matching enhances impact localization accuracy on multilayer honeycomb structures.

## Key findings

- The proposed method achieves an average localization accuracy of 8.53 mm after applying wavelet denoising and outlier weighting.
- Error outlier weighting alone improves accuracy to 12.36 mm, while the basic method yields 21.29 mm.
- The method effectively mitigates signal instability in honeycomb sandwich panels for structural health monitoring.

## Abstract

What are the main findings?
The proposed method can be applied to the localization of low-velocity impacts on honeycomb sandwich panels, and can achieve high positioning accuracy.The application of wavelet denoising and error outlier weighting in template matching method can improve the positioning accuracy.

The proposed method can be applied to the localization of low-velocity impacts on honeycomb sandwich panels, and can achieve high positioning accuracy.

The application of wavelet denoising and error outlier weighting in template matching method can improve the positioning accuracy.

What is the implication of the main finding?
This work enhances the impact localization accuracy of multilayer honeycomb sandwich panels commonly used in aerospace by mitigating noise and overcoming signal attenuation, thereby enhancing flight safety and providing a structural health monitoring solution for thick multilayer structures.

This work enhances the impact localization accuracy of multilayer honeycomb sandwich panels commonly used in aerospace by mitigating noise and overcoming signal attenuation, thereby enhancing flight safety and providing a structural health monitoring solution for thick multilayer structures.

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.