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 improved simulated annealing neural network

作者：Jingwen, Tian[1,2]; Meijuan, Gao[1,2]; Jin, Li[2]

第一作者：Jingwen, Tian

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

通讯机构：[1]Department of Automatic Control, Beijing Union University, Beijing, China|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：2006 International Conference on Communication Technology, ICCT '06

会议日期：November 27, 2006 - November 30, 2006

会议地点：Guilin, China

语种：英文

外文关键词：Corrosion - Frequency domain analysis - Neural networks - Sensor data fusion - Simulated annealing - Time domain analysis - 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 improved simulated Annealing artificial 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.