文献类型：会议论文

英文题名：Corrosion detection system for oil pipelines based on multi-sensor data fusion by wavelet neural network

作者：Jingwen, Tian[1]; Meijuan, Gao[1]; Hao, Zhou[2]; Kai, Li[2]

通讯作者：Jingwen, T.

机构：[1] Department of Automatic Control, Beijing Union University, Beijing, China; [2] School of Information Science, Beijing University of Chemical Technology, Beijing, China

第一机构：北京联合大学城市轨道交通与物流学院

会议论文集：2007 IEEE International Conference on Control and Automation, ICCA

会议日期：May 30, 2007 - June 1, 2007

会议地点：Guangzhou, China

语种：英文

外文关键词：Corrosion prevention - Neural networks - Sensor data fusion - Wavelet transforms

摘要：A system to detect the corrosion of submarine oil pipeline is introduced, it got the original data by 3 groups ultrasonic sensors and flux leakage 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 corrosion degrees of oil pipeline among of multi attribute parameters. The wavelet neural network was used to do multisensor data fusion to detect the corrosion degrees of submarin oil transportation pipelines and those key attribute parameters were used to as input vectors of network. The experimental results show that this method is feasible and effective. ? 2007 IEEE.