Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signals
Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signals
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DOI：
10.1016/j.compstruct.2023.116876
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发表时间：
2023-03
影响因子：
6.3
通讯作者：
Shirsendu Sikdar;W. Ostachowicz;A. Kundu
中科院分区：
文献类型：
--
作者：
Shirsendu Sikdar;W. Ostachowicz;A. Kundu
本文提出了一种基于深度学习架构的结构健康监测新策略，该策略使用非线性超声信号自动评估轻质加筋复合材料板（SCPs）的呼吸样粘结。为此，利用固定的压电换能器（致动器/传感器）网络，在多个复合面板上进行了超声导波（GW）响应的非线性有限元模拟和基于实验室的实验。时域的GW信号采集自scp上的传感器网络，这些信号在频域表现为存在高次谐波的非线性特征。使用连续小波变换将这些高谐波信号从原始信号中分离出来，并转换为时频尺度图图像。设计了一种深度学习架构，使用卷积神经网络自动提取离散图像特征，用于表征健康和可变呼吸剥离条件下的SCP。所提出的深度学习辅助健康监测策略对这种受多级呼吸剥离区域影响的复杂结构具有高精度的自主检测潜力。
This paper presents a new structural health monitoring strategy based on a deep learning architecture that uses nonlinear ultrasonic signals for the automatic assessment of breathing-like debonds in lightweight stiffened composite panels (SCPs). Towards this, nonlinear finite element simulations of ultrasonic guided wave (GW) response of SCPs and laboratory-based experiments have been undertaken on multiple composite panels with and without baseplate-stiffener debonds using fixed a network of piezoelectric transducers (actuators/sensors). GW signals in the time domain are collected from the network of sensors onboard the SCPs and these signals in the frequency domain represent nonlinear signatures as the existence of higher harmonics. These higher harmonic signals are separated from the GWs (raw) and converted to images of time–frequency scalograms using continuous wavelet transforms. A deep learning architecture is designed that uses the convolutional neural network to automatically extract the discrete image features for the characterization of SCP under healthy and variable breathing-debond conditions. The proposed deep learning-aided health monitoring strategy demonstrates a promising autonomous inspection potential with high accuracy for such complex structures subjected to multi-level breathing-debond regions.