An intelligent fault diagnosis method based on curve segmentation and SVM for rail transit turnout
Wenjiang Ji, Cheng Chen, Guo Xie, Lei Zhu, Yichuan Wang, Long Pan, Xinhong Hei
Abstract
With the development of intelligent transportation system, the maintenance of railway turnout is an essential daily task which was required to be efficiency and automatically. This paper presents an intelligent diagnosis method based on deep learning curve segmentation and the Support Vector Machine. Firstly, we studied the curve segmentation approach of the real-time monitoring power data collected form turnout, for which is an essential step and do a great help to improve the diagnose accuracy. Then based on the well pre-processed data sets, the SVM algorithm was applied to classify the samples and report the health states of the turnout which under testing. At last, the experiments were taken on the power data curve collected from the real turnouts, during which we compared the new diagnose method with conventional ones, and the results showed that the diagnose accuracy of proposed method can averaged to 98.5%. Compared with traditional SVM based frameworks, the proposed diagnosis method dramatically improves the accuracy which is more suitable for railway turnout.