Predicting vertical acceleration of railway wagons using regression algorithms

The performance of rail vehicles running on railway tracks is governed by the dynamic behaviors of railway bogies, particularly in cases of lateral instability and track irregularities. To ensure reliable, safe, and secure operation of railway systems, it is desirable to adopt intelligent monitoring systems for railway wagons. In this paper, a forecasting model is developed to investigate the vertical-acceleration behavior of railway wagons that are attached to a moving locomotive using modern machine-learning techniques. Both front- and rear-body vertical-acceleration conditions are predicted using popular regression algorithms. Different types of models can be built using a uniform platform to evaluate their performance. The estimation techniques' performance has been measured using a set of attributes' correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE), and computational complexity for each of the algorithms. Statistical hypothesis analysis is applied to determine the most suitable regression algorithm for this application. Finally, spectral analysis of the front- and rear-body vertical condition is produced from the predicted data using the fast Fourier transform (FFT) and is used to generate precautionary signals and system status that can be used by a locomotive driver for necessary actions

Rule-based classification approach for railway wagon health monitoring

Modern machine learning techniques have encouraged interest in the development of vehicle health monitoring systems that ensure secure and reliable operations of rail vehicles. In an earlier study, an energy-efficient data acquisition method was investigated to develop a monitoring system for railway applications using modern machine learning techniques, more specific classification algorithms. A suitable classifier was proposed for railway monitoring based on relative weighted performance metrics. To improve the performance of the existing approach, a rule-based learning method using statistical analysis has been proposed in this paper to select a unique classifier for the same application. This selected algorithm works more efficiently and improves the overall performance of the railway monitoring systems. This study has been conducted using six classifiers, namely REPTree, J48, Decision Stump, IBK, PART and OneR, with twenty-five datasets. The Waikato Environment for Knowledge Analysis (WEKA) learning tool has been used in this study to develop the prediction models

Software package applications for designing rail freight interchanges

Ph.D. ThesisRail freight transport has a crucial role to play in the economy, delivering significant reductions in logistics costs, pollution, and congestion. Typically, the conventional architecture and layout of the rail freight interchange constrain the capacity and performance of the whole railway system. A well-designed rail freight interchange can enhance the system performance by maximizing vehicle usage and minimizing last mile distribution cost. Therefore, the study of rail freight interchange operation is considered crucial to understand how to increase and improve the attractiveness for rail freight transport. This thesis uses game engines to develop software packages that are used for the design of new rail freight interchanges, considering multistakeholder decisions drivers. A novel and modular approach has been applied with the purpose of developing and deploying simulation tools that can be used by multiple stakeholders to: -Understand the impact of multiple-criteria decision analysis on rail freight interchange layouts; -Use a genetic algorithm to identify the most suitable components of the future interchange to be designed, considering the multi-stakeholders’ priorities; - Quickly enable the design of a wide variety of rail freight interchanges from the information selected by a decision maker in a computer-based userfriendly interface. This research has proposed a framework for software development. Three case studies are used to illustrate adaptability of a number of applications for different scenarios. The findings of the research contribute to a better understanding of the impacts of the multiple stakeholder’s decisions on rail freight interchange designs. Key words: Rail Freight Interchanges, Multi stakeholders decision, genetic algorith

Railway Infrastructure Capacity in the Open Access Condition: Case Studies on SŽDC and ŽSR Networks

The railway sector in the European Union is changing. The goal of EU transport policy is to liberalize the market for rail transport services, dismantle national transport monopolies, and open competitive public tenders to other train operators. For the optimal utilization of the railway infrastructure capacity, it is necessary to calculate it properly in terms of open access to the infrastructure. At present, many important corridors are at full capacity. Therefore, in order to increase the number of freight trains, it is necessary to implement certain measures to increase the track line capacity. Infrastructure capacity research is part of the complexity of the capacity management processes. A progressive approach to define it means to describe the estimating process of railway infrastructure capacity including progressive capacity allocation approaches as a key part of capacity management. The aim is to define the processes of the infrastructure capacity management on which depends the quality level of operational traffic management as well the efficiency of the traffic flow on the infrastructure. The partial objective is to investigate the impact of systematic train paths in periodic timetables on rail infrastructure capacity. The proposals fully respect the EU transport policy

Working Capital Management of Cement Industry in India - A Comparative Analysis of Selected Units

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Modelling degradation processes of switches & crossings for maintenance & renewal planning on the Swiss railway network

Approximately 25 percent of the budget for the maintenance and renewal of railway tracks –in Switzerland more than a billion Swiss Francs– is used for the switches (points) and crossings (S&C). While in the mean time the budget expenditure for the maintenance and renewal of plain track is optimized with e.g. the help of decision support systems, for S&C this optimisation stays rather rudimentary. One of the identified causes thereof is the lack of insight in the degradation and deterioration process of S&C. This study therefore combined several databases of the Swiss Federal Railways (SBB CFF FFS). Statistical analyses are carried out on them to retrieve the lifetime expectancy of complete railway switches (points) & crossings and their respective components, e.g. point rails, stock rails, frog. The expected lifetimes are attributed to different parameters which influence the speed of geometrical degradation or wear of the material, e.g. total train loads (expressed in cumulative tonnages), axle loads, the main direction of the trains, the speed and the quality of the foundation