小波支持向量机在结构损伤识别中的应用研究

基于小波框架理论和支持向量核函数的条件,引入非线性小波基函数构造支持向量机（SVM）的核函数．得到一种具有较强泛化能力的紧致型小波支持向量机。对结构在环境脉动下的反应信号进行小波包分解,利用“能量一损伤状态”的特征提取方法得到特征向量,并作为紧致型小波支持向量机的输人进行训练和分类检验,提出了一种基于完全小波支持向量机的结构损伤识别方法。以一空间单层网壳结构为检测和诊断对象,用该方法对结构的损伤位置和程度进行识另口和分类具有较高的精度,同时该方法具有面向工程实际应用、成本低和分析简便等特点。

Application of Wavelet Support Vector Machine to Structural Damage Detection

Based on the wavelet decomposition and conditions of the support vector kernel function,a nonlinear wavelet basis is introduced to construct the kernel function of support vector machine(SVM),and a compact wavelet support vector machine(WSVM) with a strong generalization ability is obtained.Wavelet packet decomposition is applied to the structural response signals under ambient vibrations,feature vectors are obtained by a feature extraction method with "energy-damage state".The feature vectors are used for training and classification as the inputs of the compact support vector machine.Hence,a new structural damage detection method called "complete wavelet support vector machine" is established.This method is used to a single-layer spherical lattice shell for damage diagnosis.The structural damage position and degree can be identified and classified,and the result is highly accurate.This approach has some advantages,such as engineering oriented,low cost and convenient.