Image processing through deep learning after DI extraction for the SHM of aeronautic composite structures using Lamb waves

Abstract

Structural health monitoring (SHM) is a crucial process that enables the diagnosis of the health state of civil and industrial smart structures through autonomous and in-situ non-destructive measurements. The focus of our study is on the damage classification step within the aeronautic context, where the primary objective is to distinguish between different damage types in composite plates. To achieve this, we considered three experimental damages -impact, delamination, and magnet -on an aeronautic composite plate embedded with a piezoelectric array and excited it using ultrasonic guided Lamb waves. We recorded signals resulting from pristine and damaged states and used three methods to create images from the raw recorded data. These methods employed Damage Indexes (DI) that compare signals in the healthy and damaged states for each actuator/sensor path. For the first two methods, images were directly created as pixel maps depicting DI distribution according to the actuator/receiver pairs over the plate. The last method applied the classical RAPID damage localization algorithm, generating damage localization maps associated with a given DI. The datasets generated by the two methods were fed into a Convolutional Neural Network (CNN) for damage classification purposes. Our study demonstrated that the best accuracy for the introduced methods was above 92% for different hyperparameters configurations, indicating their ability to perform the desired SHM damage classification task. The DI-based approach was much more efficient than the RAPID-based method, which was not intuitively expected. These findings contribute to the development of effective SHM techniques for aeronautic composite plates, paving the way for further improvements in this critical field.