The work presented in this thesis was motivated by the desire of the railway industry to capitalise on new technology developments promising seamless integration of distributed data. This includes systems that generate, consume and transmit data for asset decision support. The primary aim of the research was to investigate the limitations of previous syntactic data integration exercises, creating a foundation for semantic system development. The objective was to create a modelling process enabling domain experts to provide the information concepts and semantic relationships between those concepts. The resulting model caters for the heterogeneity between systems supplying data that previous syntactic approaches failed to achieve and integrate data from multiple systems such that the context of data is not lost when centralised in a repository. The essence of this work is founded on two characteristics of distributed data management; the first is that current Web tools, like XML, are not effective for all aspects of technical interoperability because they do not capture the context of the data; and second, there is little relationship between conventional database management systems and the data structures that are utilised in Web based data exchange which means that a different set of architecture components are required.

Railways worldwide have experienced unprecedented growth over the last decade. The infrastructure of many European railways is becoming increasingly saturated, while the operators face strong demands to increase services and carry more passengers and freight. There has been a high demand for performance improvement, to provide better service to customers. This problem is drawing increasing attention from many parties within and beyond the railway industry. In this research, the author first reviewed and analysed the existing performance measures used in railway transportation. A new generic framework for the measurement and improvement of railway network performance has been proposed, based on the concept of quality of service. The key factors affecting quality of service are identified and analysed. Secondly, the quantification of performance has been identified based on the Quality of Service framework. Multi criteria decision making has been applied to determine the weights of each Key Performance Indicator in the framework. Finally, factors within the railway system have been analysed for their impact on performance with a sensitivity analysis, to support the development of performance improvement plans. A case study has been conveyed to show the influence of the system properties on the performance measured by quality of service.

The technical suitability of hydrogen for railway traction is evaluated in the research. World-wide, diesel fuel is currently the main source of energy for railways but relies on petroleum and emits greenhouse gases. Hydrogen as a secondary energy can be produced from fossil fuels or renewables. Thus, a decrease or elimination of emissions is possible, and reliance on petroleum reduced. A well-to-wheel analysis for diesel, electricity, and hydrogen was conducted to establish overall efficiencies and emissions. The results show that hydrogen fuel cell traction has a similar efficiency to electric traction and reduces carbon emissions compared to diesel and in some case electric railways, if the hydrogen feedstock is natural gas. Renewable hydrogen would reduce emissions further. A prototype hydrogen-powered locomotive, the first in the UK, was constructed and employed for a performance evaluation; both demonstrated that hydrogen is suitable for railway traction. Computer modelling of a diesel-electric regional train, a hydrogen-powered vehicle, and a hydrogen-hybrid version was conducted. The journey time and the range of all three options was the same while energy reductions of 34 % for the hydrogen-only and 55 % for the hydrogen-hybrid, compared to the diesel were achieved.

Point machines are the key actuator used in railways to provide a means of moving a switch blade from one position to the other. Failure in the point actuator has a significant effect on train operations. Condition monitoring systems for point machines have been therefore implemented in some railways, but these condition monitoring systems have limitations for detecting incipient faults. Furthermore, the majority of condition monitoring systems which are currently in use cannot diagnose faults. The ability to diagnose faults is useful to maintenance staff who need to fix problems immediately. This thesis proposes a methodology to detect and diagnose incipient faults using an advanced algorithm. In the main body of this thesis the author considers a new approach using Wavelet Transforms and Support vector machines for fault detection and diagnosis for railway electrical AC point machines operated in Japan. The approach is further enhanced with more data sets collected from railway electrical DC point machines operated in Great Britain. Furthermore, a method to express the qualitative features of healthy and faulty waveforms was proposed to test the transferability of the specific algorithm parameters from one instance of a point machine to another, which is tested on railway electrical DC point machines used in Great Britain. Finally, an approach based on Wavelet Transforms and Neural networks is used to predict the drive force when the point machine is operating. The approach was tested using electrical DC point machines operated in Great Britain. It is shown through the use of laboratory experimentation that the proposed methods have potential to be used in a real railway system.

The optimisation algorithm has been further investigated to understand the influence of the weight coefficients that affect the solution of all the optimisation problems and it is very often overlooked in the traditional approach. In fact, the choice of weight coefficients leading to the optimum among different optimal solutions also presents a challenge and this specific problem does not give any a priori indications. This challenge has been tackled using both genetic algorithms and particle swarm optimisations, which are the best methods when there are multiple local optima and the number of parameters is large. The results show that, when the optimal set of coefficients are used and the optimal positions and capacitances of EDLCs are selected, the energy savings can be up to 42%. The second problem of the control of the storage has been tackled with a linear state of charge control based on a piece-wise linear characteristic between the current and the voltage deviation from the nominal voltage of the supply at the point of connection of the storage. The simulations show that, regardless of the initial state of charge, the control maintain the state of charge of EDLCs within the prescribed range with no need of using the on-board braking resistor and, hence, dissipating braking energy. The robustness of the control algorithm has been verified by changing the characteristics of the train loading and friction force, with an energy saving between 26-27%.

Railway transportation is facing increasing pressure to reduce the energy demand of its vehicles due to increasing concern for environmental issues. This thesis presents studies based on improved power management strategies for railway traction systems and demonstrates that there is potential for improvements in the total system energy efficiency if optimised high-level supervisory power management strategies are applied. Optimised power management strategies utilise existing power systems in a more cooperative and energy-efficient manner in order to reduce the total energy demand. In this thesis, three case studies in different research scenarios have been conducted. Under certain operational, geographic and physical constraints, the energy consumed by the train can be significantly reduced if improved control strategies are implemented. This thesis proposes a distance based model for train speed trajectory optimisation. Three optimisation algorithms, Ant Colony Optimisation (ACO), Genetic Algorithm (GA) and Dynamic Programming (DP), are applied to search for the optimal train speed trajectory, given a journey time constraint. The speed at each preset position along the journey is determined and optimised using these searching algorithms. In a DC railway network, power peaks in a substation are not desirable as they could present safety risks and are not energy efficient. A power peak can be avoided if the control of multiple trains is coordinated. The allocation of inter-station journey time intrinsically affects both service quality and energy efficiency. By identifying an optimal journey time allocation, a multi-objective function targeting both energy efficiency and service quality can be used. In this study, a DC railway is modelled with two parallel railway tracks, five station stops and three DC electric substations. Regenerative braking is studied in this DC electric network using Nodal Analysis (NA) and the Load Flow (LF) method. This study demonstrates that within the neighbourhood of an electric railway network, the journey time allocation for inter-station journeys will affect the total service quality and the energy loss. A GA is applied to find the best inter-station journey time allocation. Finally, this thesis explores the potential of applying advanced power management strategies to a Diesel Multiple Unit (DMU) train. DMU trains have multiple diesel engines which are commonly operated in a homogenous manner. The work presented in this thesis analyses the potential energy savings that may be obtained through the independent operation of the engines. Two widely investigated power management strategies which have been applied to the control of Hybrid Electric Vehicles are studied for a typical DMU railway vehicle. DP is applied to identify the optimal instant power distribution between engines. Based on the optimised results from DP, an adaptive rule-based online strategy is proposed using a non-linear programming optimisation algorithm.

This thesis presents a new experimental technique to determine the structure of train slipstreams. The highly turbulent, non stationary nature of slipstreams make their measurement difficult and time consuming as in order to identify the trends of behaviour several passages of the train have to be made. The new technique has been developed in order to minimise considerably the measuring time. It consists of a rotating rail rig to which a 1/50th scale model of a train is attached. Flow velocities are measured using two multi-hole Cobra probes, positioned close to the sides and top of the model. Tests were carried out at different model speeds. Velocity time histories for each configuration were obtained from ensemble averages of the results of a large number of runs (of the order of 80). From these it was possible to define velocity and turbulence intensity contours along the train as well as the displacement thickness of the boundary layer, allowing a more detailed analysis of the flow. Wavelet analysis was carried out on the experimental data to reveal details of the unsteady flow structure around the vehicle. It is concluded that, although this methodology introduces some problems the results obtained with this technique are in good agreement with previous model and full scale measurements.

The existing temperature profile in Great Britain determines the stress free temperature of continuous welded rail on the railway network, this ensures that cold-related tension cracks and heat-related rail buckles are kept within tolerable levels. It is predicted that winters will become warmer and summers hotter than the baseline climate. It is believed that Great Britain can continue to operate with a stress free (rail) temperature of 27°C under future climate scenarios, provided the acceptable operating range is narrowed upwards towards 27°C and that the quality of track, track-bed and subgrade are improved. These actions should limit the potential damage caused by more challenging temperature extremes. If changes are not made to ensure the track is more resilient to hotter summers the cost of buckles and heat related delays are projected to increase from £3.3m under baseline climate conditions to £24.7m in the 2080s under the high emissions climate change scenario. In winter the temperature range that causes the majority and most severe ice and snow delays is not expected to undergo much change for most of Great Britain until the 2080’s under the high emissions scenario, when there will be nominal reductions, mostly in the south region.

Abrupt changes in track stiffness can occur where the ballasted track transitions from soft foundations onto bridges and viaducts, onto hard foundations and into tunnels, or at the interface between bal¬lasted track and slab track. Such interfaces between areas of different track stiff¬ness are defined as critical zones and can cause passenger discomfort, unpredictable track degradation and, potentially, track support failures. Given their nature, main¬taining critical zones is more expensive than maintaining plain line track and measuring and assessing the variations in track stiffness are critical activities in track main¬tenance and in ascertaining the quality of newly designed and built railways. Existing methods of track stiffness measurement are not sufficiently accurate and depen¬dable. Therefore, the aim of this research was to develop an innovative but rapid method of assessing track stiffness, so as to identify problems in transition zones. During her doctoral research, the author designed and built a sleeper deflection measurement system as a means of quickly assessing track stiffness. The laser-based digital system developed uses position sensitive detectors (PSDs) and a line laser. The performance of the new measurement system was verified on a private railway at Long Marston, which showed that the system was able to capture the displacement data of individual sleepers and thus the continuous movement of rails supported by 18 sleepers. Ob¬serving the train-induced dynamic deflection patterns across multiple sleepers allows a rapid assessment of the track stiffness and provides the information that is necessary to manage the stiffness variation around transition zones.

At present, high speed dual purpose rail/road vehicles employing fixed non-destructive testing (NDT) sensors are used to inspect rails. Due to the uncertainties in characterisation of the defects when they are detected at high speed, manual re-visiting of the defects by expert operators is required before any decision regarding track maintenance is made. This research has been driven by a desire from the rail industry for a robotic system performing faster than human operators and being capable to both detect and characterise rolling contact fatigue (RCF) cracks in rails with the aim of automating the existing manual inspection and enhancing its accuracy and reliability. This thesis combines expert systems technologies with robotic NDT to fulfil this aspiration. A great deal of effort has been spent to develop a robotic inspection trolley which can automatically detect and characterise the RCF cracks in rails using an alternating current field measurement (ACFM) sensor. It uses a rule based expert system (RBES) proposed to control the robotic trolley and more importantly process ACFM data for both detecting and sizing defects. The developed system can detect the possible presence of defects in railway tracks at high speed pass (5-20 km/h) and can automatically return to an identified defect location to perform a slower and more detailed scan (up to 20 mm/s) across a rail section to determine the size, depth and number of cracks present in that section.