@inproceedings{ce38acbec2fc49899feaf83f98f7f15c,
title = "Switch Machine Fault Diagnosis Method Based on Kalman Filter and Support Vector Machines",
abstract = "Switch machines are used for operating railway turnout; its error can cause delays, increase operating costs and may even lead to train accidents. Therefore, the fault diagnosis technology for the switch machine has received more and more attention. This paper proposes a fault diagnosis method based on the action current of switch machine. Firstly, the Kalman filter is used to preprocess the collected action current to reduce the influence of the unavoidable error of the measurement. In addition, we can further improve the accuracy of fault diagnosis by extracting the characteristics of the action current curve, like the maximum, minimum and average value, etc. Finally, we use DAG-SVMs to intelligently diagnose switch failures. Experiments show that the accuracy of classification after Kalman filter preprocessing is better than that of direct classification.",
keywords = "DAG-SVMs, Feature extraction, Kalman filter, Switch machines",
author = "Xiang Li and Yong Qin and Zhipeng Wang and Jiayu Kan and Xiaofeng Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.; 4th International Conference on Electrical and Information Technologies for Rail Transportation, EITRT 2019 ; Conference date: 25-10-2019 Through 27-10-2019",
year = "2020",
doi = "10.1007/978-981-15-2866-8\_69",
language = "英语",
isbn = "9789811528651",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer",
pages = "727--735",
editor = "Yong Qin and Limin Jia and Baoming Liu and Zhigang Liu and Lijun Diao and Min An",
booktitle = "Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation, EITRT 2019 - Rail Transportation System Safety and Maintenance Technologies",
address = "德国",
}