基于深度学习的压电智能接口在各种退化情况下的功能评估

IF 2.1 3区 工程技术 Q2 ENGINEERING, CIVIL
Thanh-Truong Nguyen, Jeong‐Tae Kim, Quoc-Bao Ta, Duc-Duy Ho, Thi Tuong Vy Phan, T. Huynh
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引用次数: 8

摘要

基于压电的智能界面技术在基于机电阻抗(EMI)的损伤检测中有着广阔的应用前景。在电磁干扰监测和损伤识别过程中,智能接口设备的操作功能是一个重要问题。在本研究中,使用基于深度学习的方法诊断智能接口中发生的常见功能退化。首先,分析讨论了功能退化对电磁干扰响应的影响。其次,选取关键结构节点作为测试结构,利用智能接口进行电磁测量;第三,建立并更新了与实验模型相对应的数值模型,再现了实测的电磁干扰响应。利用更新后的数值模型,模拟了智能界面在剪切滞后效应、胶粘剂脱落、传感器断裂和界面脱落等常见功能退化情况下的电磁干扰响应;然后,对功能降解引起的电磁干扰变化进行了表征。最后,提出了一种基于卷积神经网络(CNN)的智能接口功能评估方法。该方法无需预处理,即可从原始电磁干扰信号中自动提取并直接学习最优特征。使用更新后的数值模型获得的数据集对CNN进行训练和测试。结果表明,即使在噪声的影响下,该方法也能成功地对智能界面中的四种常见缺陷进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning-based functional assessment of piezoelectric-based smart interface under various degradations
The piezoelectric-based smart interface technique has shown promising prospects for electro-mechanical impedance (EMI)-based damage detection with various successful applications. During the process of EMI monitoring and damage identification, the operational functionality of the smart interface device is a major concern. In this study, common functional degradations that occurred in the smart interface are diagnosed using a deep learning-based method. Firstly, the effect of functional degradations on the EMI responses is analytically discussed. Secondly, a critical structural joint is selected as the test structure from which EM measurement using the smart interface is conducted. Thirdly, a numerical model corresponding to the experimental model is established and updated to reproduce the measured EMI responses. By using the updated numerical model, the EMI responses of the smart interface under the common functional degradations, such as the shear lag effect, the adhesive debonding, the sensor breakage, and the interface detaching, are simulated; then, the functional degradation-induced EMI changes are characterized. Finally, a convolutional neural network (CNN)-based functional assessment method is newly proposed for the smart interface. The CNN can automatically extract and directly learn optimal features from the raw EMI signals without preprocessing. The CNN is trained and tested using the datasets obtained from the updated numerical model. The obtained results show that the proposed method was successful to classify four types of common defects in the smart interface, even under the effect of noises.
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来源期刊
Smart Structures and Systems
Smart Structures and Systems 工程技术-工程:机械
CiteScore
6.50
自引率
8.60%
发文量
0
审稿时长
9 months
期刊介绍: An International Journal of Mechatronics, Sensors, Monitoring, Control, Diagnosis, and Management airns at providing a major publication channel for researchers in the general area of smart structures and systems. Typical subjects considered by the journal include: Sensors/Actuators(Materials/devices/ informatics/networking) Structural Health Monitoring and Control Diagnosis/Prognosis Life Cycle Engineering(planning/design/ maintenance/renewal) and related areas.
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