Condition monitoring of railway transition zones using acceleration measurements on multiple axle boxes: Case studies in the Netherlands, Sweden, and Norway

Railway transition zones connecting conventional embankments and rigid struc-tures, such as bridges and tunnels, usually degrade much faster than other railway sections. Efficient health condition monitoring of transition zones is important for preventative track maintenance. In this paper, a methodology for monitoring rail-way transition zones...

Intelligent Data Fusion for Anomaly Detection in Dutch Railway Catenary Condition Monitoring

Aiming to handle the increasing variety and volume of railway infrastructure monitoring data, this paper explores the use of intelligent data fusion methods for automatic anomaly detection of railway catenaries. Three classical data dimensionality reduction methods, namely the principal component analysis (PCA), the autoencoder neural network,...

Predicting traction return current in electric railway systems through physics-informed neural networks

This paper addresses the problem of determining the distribution of the return current in electric railway traction systems. The dynamics of traction return current are simulated in all three space dimensions by informing the neural networks with the Partial Differential Equations (PDEs) known as telegraph equations. In addition, this work...

Action-driven Reinforcement Learning for Improving Localization of Brace Sleeve in Railway Catenary

Brace Sleeve (BS) plays an essential role in connecting and fixing cantilevers of railway catenary systems. It needs to be monitored to ensure the safety of railway operations. In the literature, image processing techniques that can localize BSs from inspection images are proposed. However, the boxes produced by existing methods can contain...

Multi-Objective Performance Evaluation of the Detection of Catenary Support Components Using DCNNs

The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about...

A condition-based maintenance methodology for rails in regional railway networks using evolutionary multiobjective optimization: Case study line Braşov to Zărneşti in Romania

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using...

Identification of the catenary structure wavelength using pantograph head acceleration measurements

For the condition monitoring of railway catenaries, the potential utilization of pantograph head (pan-head) vertical acceleration instead of pantograph-catenary contact force is discussed in this paper. In order to establish a baseline of the pan-head acceleration before it can be used for health condition monitoring, one of the essential...

Intelligent condition monitoring of railway catenary systems: A Bayesian Network approach

This study proposes a Bayesian network (BN) dedicated for the intelligent condition monitoring of railway catenary systems. It combines five types of measurements related to catenary condition, namely the contact wire stagger, contact wire height, pantograph head displacement, pantograph head vertical acceleration and pantograph-catenary contact...