Wavelet Network Approach for Structural Damage Identification Using Guided Ultrasonic Waves
Wavelet Network Approach for Structural Damage Identification Using Guided Ultrasonic Waves
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DOI：
10.1109/tim.2014.2299528
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发表时间：
2014-07-01
影响因子：
5.6
通讯作者：
Sadri, Amir Reza
中科院分区：
文献类型：
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作者：
Hosseinabadi, Hossein Zamani;Nazari, Behzad;Sadri, Amir Reza
提出了一种基于超声导波（GUW）信号的结构损伤定位和损伤程度检测的小波网络方法。提出了一种多输入多输出固定网格小波网络（FGWN）的构造算法。该算法包括三个主要阶段：1）小波格的形成; 2）小波矩阵的形成; 3）用正交最小二乘算法优化小波结构。从GUW信号中提取了三个损伤敏感特征：1）飞行时间; 2）归一化损伤波幅度; 3）归一化损伤波面积。这些特征被认为是FGWN的输入和损伤的位置和严重程度的估计。所建立的FGWN用于识别结构梁中的损伤位置和严重程度。在不同的损伤条件下，梁进行了研究和模拟。计算的有限元法（FEM）模拟信号用于训练FGWN。另外，还利用有限元仿真信号和实测的实验信号进行了测试。提出的损伤识别方法进行了比较，三个人工神经网络（ANN）为基础的算法。除了本文讨论的基于小波神经网络的算法的一些其他好处，结果表明，我们的方法在损伤定位和严重程度检测方面都优于其他方法。
An appropriate wavelet network (WN) approach is introduced for detecting damage location and severity of structures based on measured guided ultrasonic wave (GUW) signals. An algorithm for establishing a multiple-input multiple-output fixed grid wavelet network (FGWN) is proposed. This algorithm consists of three main stages: 1) formation of wavelet latticel; 2) formation of wavelet matrix; and 3) optimizing the wavelet structure by means of orthogonal least square algorithm. Three damage-sensitive features are extracted from the GUW signals: 1) time of flight; 2) normalized damage wave amplitude; and 3) normalized damage wave area. These features are considered as the FGWN inputs and the damage location and severity are estimated. The established FGWN is used for identifying damage location and severity in a structural beam. The beam is investigated and simulated in different damaged conditions. Computed finite element method (FEM) simulation signals are used for training the FGWN. Some other FEM simulation signals, as well as measured experimental ones are used for testing. The proposed damage identification method is compared with three artificial neural network (ANN)-based algorithms. In addition to some other benefits of the proposed WN-based algorithm over ANN-based methods discussed in this paper, the results show that our approach performs better in both damage location and severity detections than other methods.