Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
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DOI：
10.1016/j.jsv.2016.10.043
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发表时间：
2017-02-03
影响因子：
4.7
通讯作者：
Inman, Daniel J.
中科院分区：
文献类型：
--
作者：
Abdeljaber, Osama;Avci, Onur;Inman, Daniel J.
几十年来，结构健康监测（SHM）和基于振动的结构损伤检测一直是土木、机械和航空航天工程师的兴趣所在。早期和细致的损伤检测一直是SHM应用的主要目标之一。经典的损伤检测系统的性能主要取决于特征和分类器的选择。虽然固定和手工制作的特征可能是特定结构的次优选择，或者无法在另一个结构上达到相同的性能水平，但它们通常需要大量的计算能力，这可能会阻碍它们用于实时结构损伤检测。本文提出了一种新颖、快速、准确的结构损伤检测系统，该系统采用一维卷积神经网络（cnn）的固有自适应设计，将特征提取和分类块融合到一个紧凑的学习体中。该方法实现了基于振动的损伤检测和实时损伤定位。该方法的优点是能够从原始加速度信号中自动提取出最优的损伤敏感特征。在大型仿真台上进行的大规模实验表明，该方法具有优异的性能，并验证了该方法的计算效率。(C) 2016 Elsevier Ltd.版权所有。
Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method. (C) 2016 Elsevier Ltd. All rights reserved.