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

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.

Keywords

• impact localization
• projective dictionary pair learning
• fiber Bragg grating
• structural sparse representation