结构损伤的小波分形神经网络检测

用神经网络进行结构损伤检测、分析的有效性在很大程度上取决于训练样本的好坏。小波变换在时域和频域都具有表征信号局部特征的能力,小波包分析利用可以伸缩和平移的可变视窗能够聚焦到信号的任意细节,因此对有损伤的结构的非线性动力特性能进行有效的分析。利用分形几何方法不依赖于系统的数学模型的特点,将分形维数与小波分析相结合,建立了结构损伤的小波分形神经网络检测方法。研究结果表明,结构不同状态下的振动信号的各频段分形维数有明显的不同,可以将振动信号的各频段分形维数作为结构损伤检测的特征量,并用神经网络将结构的不同状态模式识别出来。

Wavelet Fractal Neural Network Method for Structural Damage Detection

The validity of structural damage detection using neural network strongly depends on the training sample.Wavelet transform is capable of signifying the detail of signal in both time region and frequency region.The wavelet packet technique focuses on each point of signal with alterable window to effectually analyze the nonlinear dynamical characteristic of damage structures.Combining fractal dimension with wavelet transform,a wavelet fractal neural network is established.The results demonstrate that in different states the fractal dimensions of structural vibration signals at various frequencies are quite distinguishable,thas the fractal dimensions of signals is able to serve as the character of structural damage detection,to identify the different structural states with neural network.