Damage imaging in skin-stringer composite aircraft panel by ultrasonic-guided waves using deep learning with convolutional neural network

Abstract

The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer’s flange region, and the stringer’s cap region. Covering the stringer’s regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.

A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures

“…Combining physical models with data models to establish a nonlinear mapping relationship between signal input and damage assessment can compensate for the shortcomings of traditional damage detection [ 120 ]. ML can be applied in several steps of ultrasonic Lamb wave damage detection, from the judgment of existence [ 121 ] to classification [ 122 ], localization [ 123 ], size assessment [ 124 ], depth reconstruction [ 125 ], and shape recognition [ 126 ]. The operational process can be summarized as obtaining detection information, extracting and selecting features, and classifying actual cases according to the categories that have been assigned labels.…”

Emergence of Machine Learning Techniques in Ultrasonic Guided Wave-based Structural Health Monitoring

“…The 1D-CNN architecture presented in this work consists of two parallel 1D-CNN layers, that can learn higher order damage-related features and improve the classification performance. Further, (Cui, Azuara, di Scalea, & Barrera, 2021) implemented a 1D-CNN algorithm for damage detection and localization in stiffened composite panels.…”

Guidelines for effective unsupervised guided wave compression and denoising in long-term guided wave structural health monitoring

“…However, this often requires that we accumulate huge datasets. 8 For example, methods based on baseline signal selection, [9][10][11] singular value decomposition, [12][13][14] principal component analysis (PCA), [15][16][17] independent component analysis, 18,19 and deep learning [20][21][22][23][24] utilize collections of previous measurements to make decisions about new measurements. As datasets grow in size, many of these methods become more effective, but also more increasingly difficult to use computationally.…”