The Detection System of Oil Tube Defect Based on Multisensor Data Fusion by Classify Support Vector Machine

Authors:
Jingwen Tian;Meijuan Gao;Kai Li
Affiliations:
Beijing Union University, China;Beijing Union University, China;Beijing University of Chemical Technology, China
Venue:
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Year:
2006

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Expert Systems with Applications: An International Journal

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Abstract

Statistical learning theory is introduced to defect detection and a detection system of oil tube defect based upon support vector machine (SVM) is presented, it got the original information by multigroup vortex sensors and leakage magnetic sensors. The oil tube defect pattern had four class that is crack, etch pits, eccentric wear and unbroken, so the multi-classify support vector machine was adopt to make the multisensor data fusion to detect the defect pattern of oil tube correctly, moreover, the genetic algorithm(GA) was used to optimize SVM parameters. The experimental results show that this method is feasible and effective.