Data-driven intelligent detection model of railway vehicle wheels flat

Abstract

For solving a large amount of data processing and reducing the cost to improve detection accuracy, this paper proposes an intelligent detection model for railway vehicle wheels flat. We propose an automatic data processing algorithm for labeling large-scale data to maximize the benefits of each experiment data. To improve the generalization of the detection model to improve the accuracy, we combine the vehicle dynamics simulation data and the simulation experiment data based on the single wheel rolling test bench to construct the data set for detecting the wheel flat. Different from the existing detection models, this paper provides a method for constructing the data sets, which is used to assist machine learning in constructing the intelligent detection model. The data set and its construction method can also promote the application of data mining in the field of railway transportation. This paper verified the effectiveness of the features obtained from data processing in the detection model. The data set is used to detect and identify the wheel flat and the recognition accuracy is 98.6%.