Estimation of Crack Parameters Through WFEM and Neural Network

摘要：

This paper presents the application of the wavelet finite element methods (WFEM) and neural network to crack parameters estimation and discusses the accuracy and efficiency of this method.The crack is presented by a rotational massless spring,and the natural frequencies for various crack parameters (location and depth) are obtained through WFEM.The neural network is then applied to establish the mapping relationship between the natural frequencies and the crack parameters,which uses feed-forward multiplayer neural networks trained by back-propagation,error-driven supervised training.With this trained neural network,the crack location and depth is estimated through using the measured natural frequencies as the input.The results of a cantilever beam experiment indicate that the estimating error of crack location is less than 3％,and the error of crack depth is less than 2％.

关键词： Crack Estimation Neural network

作者: Li Bing Zhuo Jie He Zhengjia

作者单位: The State Key Laboratory for Manufacturing Systems Engineering,Xian Jiaotong University,Xian 71004 Institute of Acoustic Engineering,Northwestern Polytechnical University,Xian 710072,China

会议类型: 国际会议

会议名称: The 8th International Conference on Damage Assessment of Structures(DAMAS 2009)(第八届结构损伤评价国际学术会议)

会议地点: 北京

会议语种:英文

页码: 31-37

在线出版日期: 2009-08-03（万方平台首次上网日期，不代表论文的发表时间）