Low-Velocity Impact Localization on a Honeycomb Sandwich Panel Using a Balanced Projective Dictionary Pair Learning Classifier.

Zhaoyu Zheng,Dakai Liang,Jiyun Lu

doi:10.3390/s21082602

Zhaoyu Zheng, Dakai Liang + Show 1 more

Open Access

https://doi.org/10.3390/s21082602

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Abstract

Carbon-fiber aluminum honeycomb sandwich panels are vulnerable to low-velocity impacts, which can cause structural damage and failures that reduce the bearing performance and reliability of the structure. Therefore, a method for locating such impacts through a sensor network is very important for structural health monitoring. Unlike composite laminates, the stress wave generated by an impact is damped rapidly in a sandwich panel, meaning that the signal qualities measured by different sensors vary greatly, thereby making it difficult to locate the impact. This paper presents a method for locating impacts on carbon-fiber aluminum honeycomb sandwich panels utilizing fiber Bragg grating sensors. This method is based on a projective dictionary pair learning algorithm and uses structural sparse representation for impact localization. The measurement area is divided into several sub-areas, and a corresponding dictionary is trained separately for each sub-area. For each dictionary, the sensors are grouped into main sensors within the sub-area and auxiliary sensors outside the sub-area. A balancing weight factor is added to optimize the proportion of the two types of sensor in the recognition model, and the algorithm for determining the balancing weight factor is designed to suppress the negative effects on the positioning of the sensors with poor signal quality. The experimental results show that on a 300 mm × 300 mm × 15 mm sandwich panel, the impact positioning accuracy of this method is 96.7% and the average positioning error is 0.85 mm, which are both sufficient for structural health monitoring.

Highlights

The honeycomb structure is a structure from nature, which has inspired research in many fields
By arranging a sensor network on the structure to measure the response signal caused by an impact, the impact can be localized via a positioning algorithm, which is very important for structural health monitoring [9,10]
This method uses (i) an Fiber Bragg grating (FBG) sensor network to record the stress-wave signal generated by an impact and (ii) a balancing weight factor (BWF) to optimize the DPL algorithm to perform sparse representation classification to localize the impact signal

Summary

Introduction

The honeycomb structure is a structure from nature, which has inspired research in many fields. A honeycomb structure can be introduced to the surface engineering of polymeric membranes; this structure can provide high solvent permeance and a stable performance [3]. Carbon-fiber aluminum honeycomb sandwich panels are lightweight, have strong load-bearing performance, and provide vibration isolation and energy absorption [4,5]. When subjected to low-velocity impacts, the honeycomb panel structures are prone to damage such as core collapse and surface layer tearing, which affects the performance and reliability of the material structure [6,7]. Fiber Bragg grating (FBG) sensors can be conveniently arranged on various material structures, have the advantages of anti-electromagnetic interference and integrated sensing and communication functions, and are very suitable for constructing a sensor network for impact positioning [11]

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