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首页> 外文期刊>International Journal of Vehicle Noise and Vibration >Applying artificial neural network and wavelet analysis for multiple cracks identification in beams
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Applying artificial neural network and wavelet analysis for multiple cracks identification in beams

机译：人工神经网络和小波分析在梁多裂纹识别中的应用

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摘要

In this research, two methods for crack detection in structures are presented and compared. The considered structure is a cantilever beam with rectangular cross section. In order to find cracks, firstly, a new technique based on wavelet analysis and finite element method (FEM) is applied. The advantage of this technique is that the crack detection process is more clear and comfortable than previous works. Then the process of crack detection is performed using FEM and combination of two types of artificial neural network (ANN) including radial basis function (RBF) and back-error propagation (BEP) neural networks. For crack identification in the proposed method, firstly, a RBF neural network is used to detect the number of cracks of structure. Then a BEP neural network is trained to detect the locations of cracks. Training of neural networks is performed using obtained data from FEM. Finally obtained results from two methods are compared with each other.
机译：在这项研究中，提出并比较了两种用于结构裂缝检测的方法。所考虑的结构是具有矩形横截面的悬臂梁。为了找到裂纹，首先，基于小波分析和有限元方法（FEM）的新技术被应用。该技术的优点是，裂纹检测过程比以前的工作更加清晰和舒适。然后使用有限元方法和包括径向基函数（RBF）和反向误差传播（BEP）神经网络在内的两种类型的人工神经网络（ANN）的组合执行裂纹检测过程。对于所提出的方法中的裂纹识别，首先，使用RBF神经网络检测结构的裂纹数量。然后训练BEP神经网络以检测裂纹的位置。使用从FEM获得的数据进行神经网络的训练。将两种方法最终获得的结果进行比较。

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