基于曲率模态和支持向量机的结构损伤位置两步识别方法

支持向量机是一种基于统计学习理论的机器学习算法,能够较好的解决小样本的学习问题。介绍了支持向量机分类和回归算法,将其应用于梁结构的损伤诊断中。以曲率模态参数作为损伤识别指标,提出了基于支持向量机的结构损伤位置两步识别方法:首先根据支持向量机分类算法的概率估计找到可能的损伤位置,重新构造训练样本;然后利用支持向量机回归算法计算精确的损伤位置。通过对悬臂梁仿真计算进行了验证,结果表明:支持向量机在结构损伤诊断领域中具有较好的应用前景。

A TWO-STEP APPROACH TO IDENTIFY DAMAGE LOCATION BASED ON CURVATURE MODAL PARAMETERS AND SUPPORT VECTOR MACHINE

Support Vector Machine (SVM) is a machine learning algorithm based on statistical learning theory, and it has recently been established as a powerful tool for classification and regression problems. This paper introduces the support vector classification and regression algorithms, which are applied to the structure damage identification. With curvature modal parameters as characteristic parameters, a two-step damage location identification approach based on support vector machine is proposed. Firstly, the possible damage location is detected by using support vector classification according to the probability distribution. Then after reconstructing the training set, the precise damage location is identified using support vector regression. The simulation results of the cantilever beam prove that this approach is a promising method for damage diagnosis.