Prediction of railway switch point failures by artificial intelligence methods

Authors : Burak ARSLAN, Hasan TİRYAKİ

Pages : 1044-1058

Doi:10.3906/elk-1906-66

View : 17 | Download : 9

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :In recent years, railway transport has been preferred intensively in local and intercity freight and passenger transport. For this reason, it is of utmost importance that railway lines are operated in an uninterrupted and safe manner. In order to carry out continuous operation, all systems must continue to operate with maximum availability. In this study, data were collected from switch motors, which are the important equipment of railways, and the related equipment and these data were evaluated with sector experience and the results related to the failure status of the switch points were revealed. The obtained results were processed with support vector machines and artificial neural networks, which are artificial intelligence methods, and machine learning was performed. In the light of this learning, a decision support model, which predicts possible failures and gives information about the root cause of the failures that have occurred, was developed. This model aims to ensure that the data obtained in each movement of the railway switch point are processed and the necessary corrective and preventive actions are communicated to the maintenance personnel; thus, failures are eliminated before they affect the railway operation and the solution process of the failures that have occurred is shortened. Considering the six switch points from which the data were collected, the experimental results were predicted with 24% RMSE error rates in the SVM method, while they were successfully predicted with RMSE error rates ranging from 2.4% to 6.6% in the ANN method. Therefore, it is observed that the ANN method is more appropriate in the implementation of the established model.
Keywords : Railway switch point, artificial neural networks, support vector machines, failure prediction, maintenance management