基于粗糙集和支持向量机的空间结构健康监测

目的 为了改进现有损伤诊断方法的不足，降低结构健康监测的成本并提高其准确性和分析速度．方法 基于小波包分解技术、粗糙集特征约简和支持向量机强大的分类功能，提出了一种空间结构损伤诊断方法，并以此为基础建立了空间结构的健康监测系统框架．利用有限元模拟，应用该方法对一单层球面网壳结构进行了损伤诊断．结果 由小波包分解得到的特征向量能够敏感地反映构件的损伤，经过学习的支持向量机基本可以正确地识别出空间结构杆件的损伤位置和程度．结论 经过粗糙集约简的损伤诊断同样有效并提高了计算速度．该方法具有面向工程实际应用、成本低和分析简便等特点。

Structural Health Monitoring for Spatial Structures Based on Rough Set and Support Vector Machine

Based on wavelet packet decomposition,rough set theory for feature reduction and the strong classifying power of support vector machine(SVM),a new approach of structural damage diagnosis for Spatial Structures is presented.The purpose is to better the existing damage diagnosis approaches,reduce the cost of structural health monitoring and improve the accuracy and speed.The frame of the spatial structural Health Monitoring system is established.The damage diagnosis used by this approach for a single-layer spherical lattice shell is simulated by finite element analysis.The results show that the feature vectors gained by wavelet packet decomposition could reflect the element damage sensitively,and the support vector machine after being instructed could identify the location and degree of the elements precisely.The damage diagnosis based on rough set reduction has nearly the same results while the computing speed is enhanced.This approach has some advantages,such as engineering orientation,low cost and convenience.