Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data

Rostislav Krč,Otto Plášek,Jan Podroužek,Martina Kratochvílová,Ivan Vukušič,Petr Dolezel

doi:10.1155/2020/8841810

Rostislav Krč, Otto Plášek + Show 4 more

Open Access

https://doi.org/10.1155/2020/8841810

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Abstract

This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.

Highlights

Railway switches and crossings (S&C) are important components of railway infrastructure
Methodology for Classification. e high cost of corrective maintenance and risk of accidents require a robust solution for train type identification as it will be part of the S&C real-time monitoring system. e S-CODE project was proposed to incorporate accelerometer signals to determine the type of passing train [6]
Results showed differences in accuracy for different scenarios, locomotives, and machine learning models which can be addressed to factors such as complex dynamic interaction of the train and S&C structure, multiple locomotive classes, similarities in locomotive undercarriage geometries, speed variance, and a relatively small amount of training data. e test scenario C that used data from one location for training and the other location for testing presented that neural network-based classifiers are generally transferable to S&C in different locations

Summary

Introduction

Railway switches and crossings (S&C) are important components of railway infrastructure. With increasing traffic and growing demands on the infrastructure, reliability and safety of S&C must be ensured. Large demands on maintenance occur especially on high-speed tracks [2]. Three different maintenance approaches can be applied—corrective, preventive, and predictive [3]. Modern predictive approaches require real-time monitoring and data collection to evaluate S&C condition and apply appropriate countermeasures when needed [4]. Accelerometer or deflection sensors are simple and reliable devices that can be mounted directly in the S&C structure for monitoring the dynamic response. Gradual changes over time for the same train type and speed may indicate an emerging defect in S&C structure [5] and provide an early warning to the infrastructure operators. Erefore, train type must be recognized from the data to evaluate changes in S&C Gradual changes over time for the same train type and speed may indicate an emerging defect in S&C structure [5] and provide an early warning to the infrastructure operators. erefore, train type must be recognized from the data to evaluate changes in S&C

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Conclusion