会议专题

Recognition of a Sucker Rods Defect with ANN and SVM

In order to improve the recognition rate of a sucker rods defect and reduce the rapture possibility of the rod, the mixed characters include of wavelet packet energy character and the peak value in the time-domain were used as the input of a recognition network, and artificial neural networks (ANN) and support vector machines (SVM) were used and compared as the recognition network to get the best recognition way. Tested results with lots of data acquired in laboratory showed that SVM was better than ANN at recognition of the sucker rods defect, and SVM based on the mixed characters can enhance recognition rate of the sucker rods defect.

Hongchun Sun Liyang Xie

School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China

国际会议

The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

三亚

英文

1053-1056

2009-04-24(万方平台首次上网日期,不代表论文的发表时间)