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
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)