期刊论文详细信息
Journal of Composites Science 卷:5
Structural Damage Detection Using Supervised Nonlinear Support Vector Machine
Kian K. Sepahvand1 
[1] School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany;
关键词: damage detection;    supervised learning;    nonlinear support vector machine;    modal analysis;    fiber-reinforced composites;   
DOI  :  10.3390/jcs5110303
来源: DOAJ
【 摘 要 】

Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.

【 授权许可】

Unknown   

  文献评价指标  
  下载次数:0次 浏览次数:0次