The Detection System for Oil Tube Defect Based on Multisensor DataFusion by Wavelet Neural Network

摘要：

A detection system of oil tube defect based on wavelet neural network is presented, it got the original information by multigroup vortex sensors and leakage magnetic sensors. We made multiscale wavelet transform and frequency analysis to multichannels original data and extracted multi-attribute parameters from time domain and frequency domain, then we selected the key attribute parameters that have bigger correlativity with the defect pattern of oil tube among of multiattribute parameters. The oil tube defect pattern had four class that is crack, etch pits, eccentric wear and unbroken. The wavelet neural network was adopt to make the multisensor data fusion to detect the defect pattern of oil tube and those key attribute parameters were used to as input of network. The experimental results show that this method is feasible and effective.

作者: Jingwen TIAN Meijuan GAO Hao ZHOU Kai LI

作者单位: Beijing Union University, China Beijing University of Chemical Technology, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）