Defects Classification of Steel Cord Conveyor Belt Based on Rough Set and Multi-class v-SVM
Because of steel cord conveyor belt with high load operating and complex conditions of coal mine, it is prone to cause conveyor belt horizontal rupture. It will bring tremendous hazards for coal mine production. Twelve time domain features of joints signals, broken wires signals and abrasion signals for steel cord conveyor belt were extracted with weak magnetic detection system. The algorithm of combining rough set based on information entropy with multiclass v-SVM based on binary tree was proposed to classify the three categories signals. The experiment results show that rough set reduction algorithm based on information entropy can effectively achieve feature reduction and classification speed of multiclass v-SVM classification algorithm based on binary tree can be improved by rough set feature reduction without changing classification accuracy.
Steel cord conveyor belt Information entropy Rough set V-SVM Classification
Hongwei MA Qinghua MAO Xuhui ZHANG Dawei ZHANG Haiyu CHEN
Xian University of Science and Technology, Xian 710054, China
国际会议
2011 International Conference on Mechatronics and Materials Processing(2011年机电一体化与材料加工国际会议 ICMMP)
广州
英文
1814-1819
2011-11-18（万方平台首次上网日期，不代表论文的发表时间）