Novel predictive model for metallic structure corrosion status in presence of stray current in DC mass transit systems
来源期刊：中南大学学报(英文版)2014年第3期
论文作者：XU Shao-yi(许少毅) 李威 XING Fang-fang(邢方方) WANG Yu-qiao(王禹桥)
文章页码：956 - 962
Key words：DC mass transit systems; stray current; corrosion; support vector machine (SVM)
Abstract: The novel method to analyze metallic structure corrosion status was proposed in the presence of stray current in DC mass transit systems. Firstly, the characteristic parameter and the influence parameters for the corrosion status were determined. Secondly, an experimental system was established for simulating the corrosion process within the stray current interference. Then, a predictive model for the corrosion status was built, using a support vector machine (SVM) method and experimental data. The data were divided into two sets, including training set and testing set. The training set was used to generate the SVM model and the testing set was used to evaluate the predictive performance of the SVM model. The results show that the relationship between the characteristic parameter and the influence parameters is nonlinear and the SVM model is suitable for predicting the corrosion status.
XU Shao-yi(许少毅), LI Wei(李威), XING Fang-fang(邢方方), WANG Yu-qiao(王禹桥)
(School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China)
Abstract:The novel method to analyze metallic structure corrosion status was proposed in the presence of stray current in DC mass transit systems. Firstly, the characteristic parameter and the influence parameters for the corrosion status were determined. Secondly, an experimental system was established for simulating the corrosion process within the stray current interference. Then, a predictive model for the corrosion status was built, using a support vector machine (SVM) method and experimental data. The data were divided into two sets, including training set and testing set. The training set was used to generate the SVM model and the testing set was used to evaluate the predictive performance of the SVM model. The results show that the relationship between the characteristic parameter and the influence parameters is nonlinear and the SVM model is suitable for predicting the corrosion status.
Key words:DC mass transit systems; stray current; corrosion; support vector machine (SVM)
