Modern railways are required to operate with a high level of safety and reliability. The weakest components are those which have the highest safety requirements and the lowest inherent reliability. Single-throw mechanical actuators, such as powered train doors, trainstops, level crossing barriers and switch actuators (point machines) are a group of components which have these properties. Preventative maintenance is carried out periodically in order to mitigate the risks of these actuators failing. This is inefficient: a condition-based maintenance approach would reduce costs and the risks to staff. However, this kind of maintenance requires very accurate automatic condition monitoring. Currently, the threshold-based condition monitoring systems installed in pilot schemes around the country do not have enough insight into actuator performance to detect incipient faults. These are hard to spot because their symptoms develop over a long period of time. It is uneconomical to carry out detailed analysis or modelling, or collect a large amount of training data, for each instance of a large group of assets. Therefore, the solution needed to establish diagnosis rules based on offline analysis, or training data from only one actuator. This thesis draws on previous work in qualitative trend analysis to build a diagnosis system which uses a combined approach of qualitative and quantitative analysis to transfer the knowledge gathered from one actuator to its fellows in service. The method used has been designed to use straightforward components, so that it can be more easily explained to users. Two case studies were carried out in order to verify the system's functions. Data were collected from real-life actuators, under simulation of incipient faults. The diagnosis system then operated on the data. The system's performance was almost as good with real-world data as it was with synthetic data. The system has been a success when operating on the data gathered under laboratory conditions. In the real world, a system such as this could be used to post-process data gathered around the railway network from actuators with local data acquisition equipment. Incipient faults could be detected in the early stages of their development and accurately diagnosed, allowing maintenance effort to be targeted very specifically, saving money, time and exposing staff to fewer hazards.

Effective maintenance of railway track is critical for the safe operation of any railway network. Efficient maintenance may also result in economic benefits for rail operators. The work in this thesis looks into how an inexpensive measurement system could be fitted to in-service railway vehicles such as commuter trains, to provide a relatively high frequency of measurement on their routes of operation, when compared to dedicated measurement vehicles. This thesis describes how a prototype inertial measurement system was designed and built, and fitted to a commuter train operating in the region south of London, UK. Inertial data is processed to provide a vertical profile of the track. A novel use of a modified Bryson-Frazier filter is used to produce vertical profile datasets which are repeatable to within 0.2 mm. Profiles calculated from multiple passes of the same areas of track are compared to show track degradation. Methods of estimating track stiffness are developed using vertical geometry data from repeated passes of the same track sections at differing speeds. Some correlation to stiffness is shown through the results, but exact measurements were not possible. Finally, two case studies are presented which show findings at a bridge approach, and through two level crossings.

A mathematical model is developed which predicts if a person or a pushchair is destabilised by a train's slipstream. The model simulates the mean slipstream velocity time history using the theories of potential flow, boundary layer growth and wake decay. The turbulence-induced fluctuations are reproduced with an autoregressive model. A randomised person is generated and subjected to the simulated slipstream, and their response is modelled by a simple solid object and a mass-spring-damper system. If the slipstream forces cause the person to be displaced by a critical distance the person is destabilised. A randomised pushchair is also generated and positioned so as to be capable of being destabilised by either toppling over or moving along the ground on its wheels. A toppling pushchair is modelled as a simple solid object in a similar manner to that of a person, and a pushchair will move along the ground on its wheels if the slipstream force is greater than the frictional force. Greater numbers of destabilised people and pushchairs are associated with the slipstream of a freightliner than that of a passenger train, increasing train speed and decreasing distance from the train side.

This thesis describes the development of a multi-train simulator in which different train control systems are simulated on a common section of high-speed line operating with four trains. The simulator is used to estimate and compare train knock-on delay performance with different signalling systems. This thesis further demonstrates a train trajectory optimisation work. Four searching approach have been implemented to find, for a specific train, the most appropriate target speed in different areas in order to minimise energy usage and delays. A West Coast Main Line case study is presented in order to assess the operational impact of using optimised train trajectory and different practical train control system configurations combined with three different operating priorities. The result shows that, by using more advanced signalling systems or optimal train trajectories, interactions between trains can be reduced, thereby improving performance. This also has the effect of reducing the energy required to make a particular journey. Simple control systems when coupled with the optimisation process have been shown to have similar performance to the more advanced signalling systems. The use of a dynamic programming allows an objective function to be minimised with the best results and an acceptable computation time.

This study aims to investigate the aerodynamic phenomena of passenger trains by undertaking a series of experimental investigations into the aerodynamics of a Class 43 high speed train (HST). A contextual research background is presented with regards to two significant aerodynamic phenomena - slipstreams and crosswinds. Model-scale experiments were undetaken on a l/25th scale HST model at the TRAIN rig moving model rig facility in order to measure slipstreams at a range of trackside positions and with different ballast heights. Crosswind effects were investigated through two model-scale tests and an extensive campaign of innovative train-based surface pressure measurements onboard an operational HST. A wind tunnel test investigated the flow field and pressure distribution around an HST power car and calculation of aerodynamic loads. A symmetrical pair of pressure taps at the train nose enabled yaw angle to be calculated at full scale. A scale-model test using a crosswind generator was undertaken and the magnitudes of aerodynamic loads compared very favorably with the wind tunnel data. The novel full scale it has been possible it isolate open-air data and gusts, and estimate the overturning forces due to crosswinds by a considered approach to surface pressure measurements.

The effectiveness and efficiency of railway vehicle condition monitoring is increasingly critical to railway operations as it directly affects safety, reliability, maintenance efficiency, and overall system performance. Although there are a vast number of railway vehicle condition monitoring technologies, wayside systems are becoming increasingly popular because of the reduced cost of a single monitoring point, and because they do not interfere with the existing railway line. Acoustic sensing and visual imaging are two wayside monitoring technologies that can be applied to monitor the condition of vehicle components such as roller bearing, gearboxes, couplers, and pantographs, etc. The central hypothesis of this thesis is that it is possible to integrate acoustic sensing and visual imaging technologies to achieve enhancement in condition monitoring of railway vehicles. So this thesis presents improvements in railway vehicle condition monitoring through the integration of acoustic sensing and visual imaging technologies.

Currently, many railway systems need to be upgraded to meet the demand for rapidly increasing railway capability, environmental concerns and customer satisfaction, while there is a lack of the right models and tools required to support the early decision making stage of railway renewal projects. In this thesis, a new railway selection upgrade process is proposed, which aims to support early stage decision-making in railway renewal projects by finding the most appropriate solutions to take forward for more detailed consideration. The railway selection upgrade process consists of modelling, simulation, split into macros-assessment and micro-simulation, and evaluation. A high-level feasibility analysis model is developed for the macro-assessment, to help engineers efficiently select the most promising upgrade options for further detailed consideration using microscopic simulation. This process provides a quick and efficient way to quantify evaluation functions, based on the 4Cs (capacity, carbon, customer satisfaction and cost) framework, to give a final suggestion on the most appropriate upgrade options. Two case studies, based on the East Coast Main Lines and the Northern Ireland railway network, are presented in order to demonstrate the application and verify the feasibility of the high-level feasibility analysis model and the railway upgrade selection process.

Britain's railway industry is implementing the vision of the system in the next thirty years, as outlined in the Rail Technical Strategy (2012); the main objectives to achieve are: carbon and cost reduction, capacity increase and customer satisfaction. The timetable design process is identified as a key enabler of the strategy's implementation. The current method in use is considered as a lengthy process with little computer support and optimisation. This study tries to overcome the outlined weaknesses of the existing method by proposing a more automated process in which the optimisation of a timetable is a properly design stage. The method has been applied to minimise the total energy consumption of five trains on the Aberdeen-Inverness line, while meeting operational and safety constraints. The results showed a reduction in the total energy consumption of 7%, while the average train total journey time is increased by 1% in comparison with the initial schedule.

Railway safety is very important, as it concerns human lives. Therefore identifying risks from possible failures is vital to maintain the safety of railways. Currently, many mature tools, such as fault tree analysis and event tree analysis, are applied to investigate possible risks to railway safety. However, in many circumstances, the applications of these tools are unable to provide satisfactory results when the risk data is incomplete or there is a high level of uncertainty involved in the risk dataThus it is essential to develop new methods to overcome the weakness of current assessment tools. This thesis introduces an improved intelligent system for risk analysis usingfuzzy reasoning approach (FRA) and improved fuzzy analytical hierarchy decision making process (Fuzzy-AHP), which is specially designed and developed for the railways, and able to deal with the uncertainty in risk assessment.

Thesis (Ph. D.)--University of Missouri-Columbia, 2002. / Typescript. Leaf v does not exist therefore pre-paging is misnumbered. Vita. Includes bibliographical references (leaves 256-262). Also available on the Internet.