Development of a small-scale low-cost SHM system for thin-walled CFRP structures based on acoustic emission analysis and neural networks

摘要

This study presents a low-cost & small-scale Structural Health Monitoring System (SHM) for thin walled carbon fiberreinforced plastics (CFRP) structures based on acoustic emission (AE) analysis. It covers the inherent geometriccomplexity and anisotropic properties of such structures through the implementation of an artificial neural network(ANN). The system utilizes piezoelectric sensors, a data acquisition unit and a microprocessor with a trained ANN inorder to localize events that result from artificial sound sources. Besides high precision in localization the system isscalable and adaptive through adequate design and training of the ANN and system hardware.Especially for CFRP, nowadays well established for lightweight applications in the aerospace and automotive industry,such a system helps to overcome their major downside, their sensitivity towards impact loading. Impact sources like birdstrikes, tool drops or stone debris can be the cause for delamination that can result in a severe drop of stiffness and earlycatastrophic failure. In order to guarantee structural integrity, CFRP structures therefore need to be inspected via nondestructivetesting methods on a regular scheme. Due to its passive nature and in-situ capabilities AE-based SHM canreduce cost and down-time that come with regular inspections as an alternative approach that allows for a conditionbasedinspection scheme.