Recognition of a Sucker Rod´s Defect with ANN and SVM

Author

Sun, Hongchun ; Xie, Liyang

Author_Institution

Sch. of Mech. Eng. & Autom., Northeastern Univ., Shenyang, China

Volume

2

fYear

2009

fDate

24-26 April 2009

Firstpage

3

Lastpage

6

Abstract

In order to improve the recognition rate of a sucker rod´s 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 rod´s defect, and SVM based on the mixed characters can enhance recognition rate of the sucker rod´s defect.

Keywords

flaw detection; image recognition; neural nets; oil technology; production equipment; rods (structures); statistical analysis; support vector machines; time-domain analysis; wavelet transforms; ANN; SVM; artificial neural network; oil pumping equipment; pattern recognition; rapture possibility; sucker rod defect recognition; support vector machine; time-domain analysis; wavelet packet energy character; Artificial neural networks; Character recognition; Corrosion; Frequency; Histograms; Noise reduction; Pattern recognition; Support vector machines; Time domain analysis; Wavelet packets;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on

Conference_Location

Sanya, Hainan

Print_ISBN

978-0-7695-3605-7

Type

conf

DOI

10.1109/CSO.2009.359

Filename

5193886