Generating Compact Geometric Track-Maps for Train Positioning Applications

In this paper, we present a method to generate compact geometric track-maps for train-borne localization applications. Therefore, we first give a brief overview on the purpose of track maps in train-positioning applications. It becomes apparent that there are hardly any adequate methods to generate suitable geometric track-maps. This is why we present a novel map generation procedure. It uses an optimization formulation to find the continuous sequence of track geometries that fits the available measurement data best. The optimization is initialized with the results from a localization filter developed in our previous work. The localization filter also provides the required information for shape identification and measurement association. The presented approach will be evaluated on simulated data as well as on real measurements

Detecting singular track defects by time-frequency signal separation of axle-box acceleration data

Singular railway track irregularities, such as squats and corrugation have a major impact on the ride stability, noise emission, comfort and safety of freight and passenger trains. Therefore, the detection and monitoring of such defects play an important role in railway track maintenance. Embedded low-cost sensors on in-service vehicles provide the opportunity of quasi-continuous condition monitoring of railway tracks and can thus enhance existing track maintenance strategies. In this paper we demonstrate a processing sequence to detect singular track defects from noisy axle-box acceleration (ABA) data. The data are acquired with a multi-sensor prototype measurement system on a shunter locomotive operating on the industrial railway network of the inland harbor of Braunschweig (Germany). A blind signal separation (BSS) algorithm based on non-negative matrix factorization is applied to the ABA data in the time-frequency domain. It is completely data-driven and hence does not rely on a priori knowledge or physical models. The algorithm makes use of different time-frequency characteristics of the signal components and is thus able to separate quasi-continuous band-limited signal components from transient broad-band components. The magnitude of the transient components reflects the strength of track singularities along the track and can hence be used to detect and quantify short track defects. Through georeferencing the identified defects can be localized, mapped on the track and be used to guide specific maintenance actions. Additionally, the BSS algorithm shows the potential to reduce the dimensionality of the data without significant loss of information

Using information engineering to understand the impact of train positioning uncertainties on railway subsystems

Many studies propose new advanced railway subsystems, such as Driver Advisory System (DAS), Automatic Door Operation (ADO) and Traffic Management System (TMS), designed to improve the overall performance of current railway systems. Real time train positioning information is one of the key pieces of input data for most of these new subsystems. Many studies presenting and examining the effectiveness of such subsystems assume the availability of very accurate train positioning data in real time. However, providing and using high accuracy positioning data may not always be the most cost-effective solution, nor is it always available. The accuracy of train position information is varied, based on the technological complexity of the positioning systems and the methods that are used. In reality, different subsystems, henceforth referred to as ‘applications’, need different minimum resolutions of train positioning data to work effectively, and uncertainty or inaccuracy in this data may reduce the effectiveness of the new applications. However, the trade-off between the accuracy of the positioning data and the required effectiveness of the proposed applications is so far not clear. A framework for assessing the impact of uncertainties in train positions against application performance has been developed. The required performance of the application is assessed based on the characteristics of the railway system, consisting of the infrastructure, rolling stock and operational data. The uncertainty in the train positioning data is considered based on the characteristics of the positioning system. The framework is applied to determine the impact of the positioning uncertainty on the application’s outcome. So, in that way, the desired position resolution associated with acceptable application performance can be characterised. In this thesis, the framework described above is implemented for DAS and TMS applications to understand the influence of positioning uncertainty on their fundamental functions compared to base case with high accuracy (actual position). A DAS system is modelled and implemented with uncertainty characteristic of a Global Navigation Satellite System (GNSS). The train energy consumption and journey time are used as performance measures to evaluate the impact of these uncertainties compared to a base case. A TMS is modelled and implemented with the uncertainties of an on-board low-cost low-accuracy positioning system. The impact of positioning uncertainty on the modelled TMS is evaluated in terms of arrival punctuality for different levels of capacity consumption. The implementation of the framework for DAS and TMS applications determines the following: • which of the application functions are influenced by positioning uncertainty; • how positioning uncertainty influences the application output variables; • how the impact of positioning uncertainties can be identified, through the application output variables, whilst considering the impact of other railway uncertainties; • what is the impact of the underperforming application, due to positioning uncertainty, on the whole railway system in terms of energy, punctuality and capacity

Intelligent in-service shunters in German harbor railways

The DLR Institute of Transportation Systems is an active member of several research projects funded by the German funding initiative IHATEC. These projects involve shunters in harbor railways that are equipped with intelligent sensor units, with different applications in focus. The paper gives a technical perspective on the multi-sensor systems to collect data on in-service vehicles, positioning algorithms that calculate the accurate geo-information which is key to intelligent applications, and advanced condition monitoring using axle-box-acceleration data

Railway Research

This book focuses on selected research problems of contemporary railways. The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. The last chapter focuses on modeling railway vehicle dynamics using numerical simulation, where the dynamical models are exploited

Damage identification in warren truss bridges by two different time–frequency algorithms

Recently, a number of authors have been focusing on drive-by monitoring methods, exploiting sensors mounted on the vehicle rather than on the bridge to be monitored, with clear advantages in terms of cost and flexibility. This work aims at further exploring the feasibility and effectiveness of novel tools for indirect health monitoring of railway structures, by introducing a higher level of accuracy in damage modelling, achieve more close-to-reality results. A numerical study is carried out by means of a FE 3D model of a short span Warren truss bridge, simulating the dynamic interaction of the bridge/track/train structure. Two kinds of defects are simulated, the first one affecting the connection between the lower chord and the side diagonal member, the second one involving the joint between the cross-girder and the lower chord. Accelerations gathered from the train bogie in different working conditions and for different intensities of the damage level are analyzed through two time-frequency algorithms, namely Continuous Wavelet and Huang-Hilbert transforms, to evaluate their robustness to disturbing factors. Compared to previous studies, a complete 3D model of the rail vehicle, together with a 3D structural scheme of the bridge in place of the 2D equivalent scheme widely adopted in the literature, allow a more detailed and realistic representation of the effects of the bridge damage on the vehicle dynamics. Good numerical results are obtained from both the two algorithms in the case of the time-invariant track profile, whereas the Continuous Wavelet Transform is found to be more robust when a deterioration of track irregularity is simulated

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

Artificial intelligence for railroads: Potential and challenges for application

It is clear that AI methods have a great deal to contribute to the solution of relevant problems. Realistic expectation management for AI solutions is important for a productive compromise between the extreme “AI can solve everything” and “AI is unusable” positions. AI tools should therefore not be considered in isolation, but approached as an effective tool that can harmonize with other methodologies. This secures a connection to established procedures and enhances acceptance by stakeholders. It is also clear that there is a need of guidelines and procedures for approving AI algorithms, especially in safety-critical applications. Since the capabilities and reliability of AI methods will continue to grow in the future, it is important to start preparing for their use by initiating suitable approval procedures