Research on Algorithm of Railway Disease Prediction

• 1、School of software, Beijing University of Posts and Telecommunications

Abstract：However, there are still considerable challenges in breaking through the traditional manual detection to quickly identify railway diseases. Due to the long-term exposure to complex and harsh natural environments and the repeated effects of train short circuits, there are many types of deformation diseases in the track structure. These diseases will cause the smoothness of the track surface to deteriorate, which will affect the smooth and comfortable operation of the train, Threatening driving safety. In order to realize the rapid location of the problem railway point with a certain accuracy, a stack fusion algorithm is used to cascade multiple machine algorithms and neural networks to perform regression prediction on existing railway data, and then locate the problem through threshold review. Railway points, so as to quickly locate railway problem points with a certain accuracy in the guarantee. The model here uses four traditional machine learning algorithms, while improving the RCNN network for text classification for prediction. By comparison, under the evaluation standard of mean square error, the result of a certain model is 0.13, and the result of this paper is close to 0.04, which is an increase of 9 percentage points.

Keywords： Neural Networks Railway disease Model fusion