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 multi-class 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 multi-class v - SVM classification algorithm based on binary tree can be improved by rough set feature reduction without changing classification accuracy.