The European Train Control System (ETCS) as part of the ERTMS (European Rail Traffic Management System) is a train control and automation system, which has been introduced into the UK rail network. The major change with the introduction of the ERTMS has been the shift of the movement authority from signals outside on the tracks to inside the cab, and the introduction of speed profiles that the drivers must adhere to. The introduction of this new system triggered the need to understand its impact on the train driving task and train driver behaviour. In particular, the effect the ERTMS has on drivers’ cognitive strategies and demands. The overall aim of the thesis was to understand the effects of new train control and automation technology on train drivers’ behaviours. The research was conducted, using a mixed methods approach, in the rail environment with train drivers and rail experts. Literature reviews of existing train driving models, train driving research and associated issues of increasing control and automation on human behaviour; were used to provide the theoretical context for the thesis. The literature review highlighted that there was a potential shift in cognitive strategies and demands with the introduction of train automation and control technology. Due to the limited amount of research in train driving on the whole, but also train driving with automation and control technology, the majority of the literature hypothesised the possible impact of the introduction of automation and control technology. An exploratory study of some of the different forms of train driving in the UK, was used to generate insight about train driving with different forms of train technologies and provided the theoretical foundations for the following studies. The emerging cognitive themes also addressed the gap in knowledge about train driving with different forms of technologies. The emerging cognitive themes from this study included route knowledge and memory, monitoring, allocation of attention, anticipation, prioritisation and decision making. A semi-structured interview study with ERTMS drivers, addressed some of the questions raised in the previous study using ERTMS drivers’ subjective experiences. Since the exploratory study, the results demonstrated an adaptation and shift towards acceptance of the system and it also identified some of the driving strategies that had emerged. This chapter investigated drivers’ subjective experience, highlighting high-level strategy changes. A real world exploratory eye-tracking study with both conventional and ERTMS drivers on their normal timetabled routes, provided a wealth of data. The first level of the quantitative eye-tracking analysis, aimed to address the industry question of ‘heads up’ vs. ‘heads up, heads down driving’. It demonstrated a shift of typical visual attentional strategy from monitoring outside on the tracks to speed information inside the cab. Analysis of verbal protocol data collected in the eye-tracking study also provided some rich qualitative data about train driver strategies and demands. Further analyses of the eye-tracking data, identified events where there is a difference in visual behaviour between ERTMS driving and conventional driving, but also between each type of driving and its own baseline data. An expert elicitation workshop with ERTMS human factors experts, was used to generate requirements for a future train driving model. The main findings highlighted that several models are needed to help address some of the issues raised, as they could provide different uses, acting as ‘building blocks’ to the overall picture. Qualitative models can be used to provide the framework and language as a communication tool, whilst more quantitative models can be used to compute error and workload. Models need to be informed by cognitive theory but also focus on the train driving tasks and information used by train drivers. Finally, the studies presented in this thesis were used to develop an integrated human factors model of the influence of train automation and control technology on train driving and guidance was generated for future train driving models for both conventional and ERTMS train driving. The current research has contributed critical knowledge to both the academic literature but also informed human factors practitioners in the rail industry. The thesis has contributed novel understanding about train driving with a control and automation technology, which have already been utilised by the rail industry.

In today’s modern world, society are accustomed to disposable products, temporary services and frequent replacements. The art of maintaining and renewing assets has been somewhat lost. However, in the pursuit of financial performance, comes the need to effectively manage assets. Management of a large portfolio of infrastructure assets is a complex and demanding task for infrastructure owners. Not only is the coordination of a large organisation difficult to align, but every decision is scrutinised by regulatory bodies. For infrastructure portfolio managers, decision support tools are becoming increasingly more useful. This is particularly relevant to railway structures as a result of their diversity and age. A thorough literature review (Chapter 2) is carried out to understand what decision support tools, known as Bridge Management Systems (BMSs), are currently available for railway bridge portfolio managers. The modelling approaches which have been used as the foundation of the BMSs are analysed (Chapter 3). Of these, the most appropriate modelling technique is selected for development of a new approach for a decision support tool. The tool comprises of a number of different modules, each with its own characteristics, data sources and features (Chapter 4). The model is presented, as well as detailed descriptions of each of the modules and how they work. During the literature review stage, a number of studies mentioned that there are external factors that affect deterioration. However, very few studies were able to pinpoint what these factors were, how much they affected deterioration and what the operational, financial and management impacts were. To that effect, a number of different factors were analysed (Chapter 5) to ascertain if they have an effect on bridge deterioration. The key factors were identified and their deterioration profiles incorporated into a probabilistic Petri-Net (PN) model, calibrated with historical data. From these, comparative model outputs pinpointing which factors affect bridge deterioration the most can be computed. Finally, simulations were carried out on the PN model to evaluate which of the factors would have the most financial effect for a transport agency. This allows bridge managers to categorize bridges in different deterioration groups allowing the definition of different optimal inspection and maintenance strategies for each group. This research has also identified that complex models often have a heavy computational burden. A study was carried out to accelerate simulations of PN models with General-Purpose Graphics Processing Units (GPGPUs)(Chapter 7). GPGPUs are composed of many smaller, parallel compute units which has made them ideally suited to highly parallelized computing tasks. The efficiency of different approaches to parallelization of the problem is evaluated. The developed framework is then used on the railway bridge PN model. The results obtained show that this method allows the combination of complex PN modelling with rapid computation in a desktop computer. A final piece of research was undertaken to perform optimisation with the railway bridge PN model (Chapter 8). This study utilised the foundation railway bridge PN model, the Local Environmental Factors (LEFs), the variability factors and the GPGPU acceleration. A Hybrid Multi-Objective Genetic Algorithm (MOGA) approach is accelerated with GPGPUs to find the optimal inspection regime to minimise both the WLCC of railway bridges and the risk of being in a poor condition. The proposed Hybrid Genetic Algorithm (GA) approach is able to accelerate the process by over 30 times compared to the traditional GA approach. The results obtained demonstrate a potential 9% reduction in overall WLCC for UK railway bridges at the same condition as the current industry policy performance. A novel Performance-Based Inspection Planning (PBIP) protocol is introduced to demonstrate where inspections should be focused to monitor bridges in areas susceptible to more severe deterioration whilst easing inspection efforts on those in milder areas of deterioration, improving operational efficiency.

It is important to monitor the wheel-rail friction coefficient in railway vehicles to improve their traction and braking performance as well as to reduce the number of incidents caused by low friction. Model based fault detection and identification (FDI) methods, especially state observers have been commonly used in previous research to monitor the wheel-rail friction. However, the previous methods cannot provide an accurate value of the friction coefficient and few of them have been validated using experiments. A Kalman filter based estimator is proposed in this research project. The developed estimator uses signals from the traction motor and provides a new and more efficient approach to monitoring the condition of the wheel-rail contact condition. A 1/5 scaled test rig has been built to evaluate the developed method. This rig comprises 2 axle-hung induction motors driving both the wheelsets of the bogie through 2 pairs of spur gears. 2 DC generators are used to provide traction load to the rollers through timing pulleys. The motors are independently controlled by 2 inverters. Motor parameters such as voltage, current and speed are measured by the inverters. The speed of the wheel and roller and the output of the DC generator are measured by incremental encoders and Hall-effect current clamps. A LabVIEW code has been designed to process all the collected data and send control commands to the inverters. The communication between the PC and the inverters are realized using the Profibus (Process Field Bus) and the OPC (Object Linking and Embedding (OLE) for Process Control) protocol. 3 different estimators were first developed using computer simulations. Kalman filter and its two nonlinear developments: extended Kalman filter (EKF) and unscented Kalman filter (UKF) have been used in these 3 methods. The results show that the UKF based estimator can provide the best performance in this case. The requirement for measuring the roller speed and the traction load are also studied using the UKF. The results show that it is essential to measure the roller speed but the absence of the traction load measurement does not have significant impact on the estimation accuracy. A re-adhesion control algorithm, which reduces excessive creepage between the wheel and rail, is developed based on the UKF estimator. Accurate monitoring of the friction coefficient helps the traction motor work at its optimum point. As the largest creep force is generated, the braking and accelerating time and distance can be reduced to their minimum values. This controller can also avoid excessive creepage and hence potentially reduce the wear of the wheel and rail. The UKF based estimator development has been evaluated by experiments conducted on the roller rig. Three different friction conditions were tested: base condition without contamination, water contamination and oil contamination. The traction load was varied to cover a large range of creepage. The importance of measuring the roller speed and the traction load was also studied. The UKF based estimator was shown to provide reliable estimation in most of the tested conditions. The experiments also confirm that it is not necessary to measure the traction load and give good agreement with the simulation results. With both the simulation and experiment work, the UKF based estimator has shown its capability of monitoring the wheel-rail friction coefficient.

Regulation requires railway energy absorbers to dissipate the collision energy and to prevent overriding. There is no industrial consensus about which energy absorbing mechanism is the most suitable for the crash conditions present in a collision between railway vehicles. There is scope for improving the existing designs or creating new concepts. The combination of two energy absorption mechanisms, expansion and splitting of cylindrical tubes, was identified as an improved energy absorption mechanism. Quasi-static and dynamic testing of scaled splitting, expansion and expansion splitting (hybrid) tubes was carried out to assess their force, stroke, energy absorption and oblique loading efficiency. In addition, the standard requires a calibrated numerical model of the energy absorber to predict its behaviour. The fracture strain of the tube and the coefficient of friction between the tube and the die are needed to build accurate numerical models. The fracture strain was measured using a Digital Image Correlation technique and a new methodology was developed to overcome its limitations. The inclusion of the fracture strain correctly predicted the deformation of the splitting specimens. The friction coefficient was adjusted until the energy absorption matched that observed during testing. Quasi-static testing showed that the force efficiency was 80%, 100% and 90%, for the splitting, expansion and hybrid tubes respectively. The stroke efficiency was measured as 77%, 44% and 70%, respectively. The energy absorption efficiency of the hybrid tubes was assessed as 11% and 40% higher than that of the splitting and expansion tubes respectively. The testing also showed that the hybrid tubes were more insensitive than the expansion and splitting tubes to the application of oblique loading. More testing may be necessary to confirm this assertion. The results suggest that the hybrid energy absorbing mechanism could become a commercial energy absorber with improved performance over the existing solutions. The validation of the hybrid numerical models showed an accurate prediction of the test results. A full-scale hybrid demonstrator has been tested and a patent of the hybrid concept applied for.

The main aim of this thesis is to investigate available techniques and develop a methodology for the fault detection and diagnostics for two engineering systems, namely railway point systems (RPS) and three-phase separators (TPS). The fault detection of the RPS was performed on the measured current from the motor of point operating equipment (POE). The method of One Class Support Vector Machines has been chosen as the fault detection model. Elastic similarity measures, such as edit distance with real penalties and dynamic time warping, were chosen to compare the data of POE operations. A combination of Euclidean distance and elastic similarity measures has been proposed in order to take into account the absolute values and shape properties of the two compared time series. The proposed methodology has been tested on the in-field RPS data. The results indicated that the fault detection model was able to detect abnormal values and/or shape of the time series of measured current. However, not in all cases these changes could be related to a recorded failure of RPS in the database. The fault detection of TPS was performed given the sensor readings of flow and level transmitters of TPS. The method of Bayesian Belief Networks (BBN) has been proposed to overcome the late detection of faults with the threshold based alarm technique. An approach to observe sensor readings of TPS in several adjacent time intervals and to update the prior probabilities in the BBN after inserting the sensor readings as evidence was proposed. The proposed methodology has been tested on the data obtained from a TPS simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the TPS.

This thesis examines the effect of vehicle dynamics on lateral deterioration of the track alignment. As rail traffic runs along a route, the forces imposed upon the track cause the ballast to settle, and hence the track geometry deteriorates. At a specified value of deterioration the track geometry needs to be restored by tamping or other methods. As the deterioration is mainly in the vertical direction, this aspect has been more widely studied and models have been developed to predict vertical track geometry deterioration. On the other hand, lateral track deterioration is not as well understood, and this thesis aims to fill the gap in this knowledge. However, the understanding of the lateral deterioration mechanisms becomes more important as speed and capacity increase. This thesis describes statistical studies of track lateral deterioration, as well as the development and validation of a vehicle-track lateral dynamic interaction model. This work is undertaken to contribute to the fundamental understanding of the mechanisms of track lateral deterioration, therefore making the effective control and reduction of the lateral deterioration achievable. The statistical analysis provides a better understanding of three aspects of track lateral irregularities, namely: the relationship between vertical and lateral irregularities, the relationship between track curvature and track lateral irregularity and the change in track lateral deterioration over time. The vertical and lateral track irregularity magnitudes are clearly correlated. The track quality in the vertical direction is generally worse than in the lateral direction, however the number of track sections with lateral quality significantly worse than the vertical is non-negligible. The lateral irregularities tend to be larger on curves. It is notable that less than ten percent of the track studied has a constant lateral deterioration due to frequent maintenance activities and bidirectional lateral dynamic forces. Unlike vertical settlement, lateral deterioration develops exponentially in both magnitude and wavelength, and the major influences are found from the irregularities with wavelength longer than 10 m. The change in track lateral irregularity with different curve radii and the lateral deterioration rate are described in separate exponential power functions due to the limitation of the available track data. The parameters for these empirical equations do not remain constant due to the change in track conditions. Current track lateral models mainly focus on lateral failures such as buckling and lateral sliding. The development of lateral track irregularities tends to be studied using representative values of net lateral forces and net L/V (Lateral/Vertical) load ratios. Unlike other track lateral deterioration models, the model developed in this thesis focuses on the development of lateral irregularities based on the dynamic interactions between the vehicles and the track system. This model makes it possible to carry out more integrations and analysis of the track lateral deterioration in a realistic dynamic simulation, using vehicle models, contact conditions, track initial irregularities, and traffic mix more close to the reality. The vehicle-track lateral dynamic interaction model was validated against track geometry data measured on the West Coast Mainline (WCML) in England. It has been found that the model gives a reasonably accurate prediction of the development of lateral track irregularities. However, it also tends to predict a short wavelength deterioration that is not seen in the actual track deterioration. Improvements to the model are suggested by either adding more factors or simplifying the model depending on specific target application. Enhancing the model by including more details, such as longitudinal forces, temperature effect, more layered track systems, uneven track bed conditions and more representative wheel-rail contact conditions etc., may help understand the reason of the additional short wavelength. A sensitivity analysis was performed in order to identify the critical factors that influence lateral track deterioration. The track damage caused by specific vehicles can be controlled by understanding different vehicle dynamics behaviour on a particular track section or route. Vehicles with simple suspension design and heavy axle loads tend to cause more lateral track damage. Within a certain speed range, there will be a critical speed that generates the largest lateral deterioration. Vehicles with different dynamic behaviours can generate a potential offset of the lateral deterioration, so it is possible to design the traffic mix to cancel out the peak deterioration. However, it may not be very practical to redesign the traffic mix due to different traffic requirements. Subsequently, actions can be taken to effectively reduce track lateral deterioration, such as optimise the suspension design, vehicle weight, the selection of an optimal operation speed, and enhance the traffic mix design. As the most important interface between vehicle and track, the wheel-rail contact condition has an extremely large influence on lateral deterioration. Wheel and rail profiles with different wear conditions can cause altered vehicle-track lateral dynamic interaction. It is found that increasingly worn wheel/rail profiles within an acceptable tolerance can effectively reduce the lateral deterioration. Lateral deterioration can also be reduced by increasing all the track stiffness values, damping values and the mass of rails and sleepers, or alternatively, by decreasing the sleeper spacing. The sleeper-ballast interface is found to play the most important role in lateral deterioration. The interfaces between the sleeper and ballast shoulder, crib and base determines the non-linear characteristic such as hysteresis and sliding features. Improving the strength of the sleeper-ballast interface can improve the elastic limits and hysteresis characteristics, hence reducing the lateral deterioration. The findings of the investigation indicate that the model provides in-depth knowledge of the mechanisms influencing lateral deterioration and provides effective solutions with consideration of vehicles, wheel-rail contact and the track system. Further work would include track data with sufficient information in order to develop a more comprehensive empirical model that describes the lateral deterioration, inclusion of more potentially influential factors such as: temperature, ground condition, traffic etc. The model can be improved by taking into account additional factors such as the influence of longitudinal forces from the wheels to the rails, different weather and temperatures, subgrade and ground conditions, etc. The reason for the high frequency noise in the deterioration prediction is not understood yet and it should be discussed in terms of more accurate vehicle simulation results and more comprehensive rail and wheel worn profiles measured on the target track and vehicles. Furthermore, the sleeper-ballast lateral characteristics are not well understood and the previous research in this area is quite limited. To improve on the present work it would be useful to carry out laboratory tests in order to capture more accurately track lateral stiffness and damping values as well as the comprehensive non-linear characteristic of track lateral residual resistance behaviour.

It is important to study accidents and their underlying causes, in order to generate recommendations for improving system safety. A range of methods have been developed in various industries, to understand how accidents have occurred, as well as identify potential human errors in systems. Theories of accident causation, and the development of safety models and methods have evolved over the last few decades. However, the majority of accident analysis methods fail to account for the increasing complexity of socio-technical systems (Hollnagel, 2004 and Lindberg et al. 2010). Much of the previous research has taken a safety I perspective, which considers successful performance as reducing the number of adverse outcomes to as low as possible (Hollnagel, 2014). According to Hollnagel (2014) however, it is important to understand how operators actually carry out work (‘work-as-done’), rather than as it should be carried out (‘work-as-imagined’), to understand how normal variabilities and flexibilities in performance contribute towards both successful and unsuccessful performance. Understanding how work is normally carried out is essential for understanding how it can go wrong. This includes understanding how success is obtained, for example how people adjust their performance in the face of changing conditions and demands, and limited resources (such as time and information). Although variability and flexibility in performance are prerequisites for success and productivity, these can also explain why things can go wrong (Hollnagel, 2014). Understanding normal work (or ‘work-as-done’) is the basis of the safety II perspective, which views safety as increasing the number of things that go right. So far however, there seems to be little application of this safety II perspective in the rail industry. Research in this thesis addresses this gap, by examining whether understanding normal performance in rail engineering contexts contributes towards identifying how incidents occur, and measures for improving safety, compared to the use of existing methods. A range of different methods were used to address the aims of this thesis. Rail incident reports were analysed to understand sources of human errors in rail contexts. Observations were also conducted of operators carrying out work, to understand the opportunities for human errors associated with rail engineering processes. To understand cognitive demands and strategies associated with normal work, a cognitive task analysis was carried out. FRAM (Functional Resonance Analysis Method) (Hollnagel, 2012) wasalso used to determine how incidents may develop, and whether everyday performance can contribute towards successful and unsuccessful performance. Participants in semi-structured interviews and workshops were asked to identify strengths and limitations of various human reliability assessment methods, and offer opinions on their practical applicability. Benefits of understanding normal work included a greater understanding of how human errors can occur (by identifying cognitive demands that contribute towards the occurrence of different error types), and how cognitive strategies can reduce human errors and contribute towards acceptable performance. It was demonstrated how variabilities and flexibilities in performance can contribute towards successful and productive performance, as well as explain why things can go wrong (supporting Hollnagel, 2014). This is especially important to consider, since human errors were not easily identified from rail incident reports and observations of operators carrying out work. System safety can therefore be improved by increasing things that can go right, rather than just decreasing the things that can go wrong (Hollnagel, 2014). Participants in a workshop, however, identified that FRAM may be time consuming to apply, especially for more complex systems. Further research is recommended for the development of a toolkit, from which both practitioners and researchers can choose from a range of different methods. To further understand factors affecting acceptable performance, it is recommended that further data are collected to determine whether varying levels of cognitive demands affect performance, and whether these influence the implementation of cognitive strategies.

M.Ing. / This report documents the steps taken to gain insight into the dynamics of a HS MkVII self-steering three piece bogie. This was done by firstly studying the dynamics and stability of linear simplifications of the bogie and wagon and then by investigating the dynamics of the bogie by means of a non-linear model.With the necessary insight into the dynamics of the bogie, an optimised relationship between the primary suspension stiffness and the centre plate friction of a self-steering three-piece bogie was achieved. The optimised model’s wear is less than half that of the reference model and has a safe operating speed of 80km/h for an empty wagon and 140 km/h for a loaded wagon. It is recommended that the following be done before issuing a final technical recommendation; • A final optimisation of the lateral and longitudinal primary suspension stiffness arrangement; taking into consideration the physical vertical load bearing capacity of the rubber suspension elements. • A study in order to quantify the effects, on wear, of the increased misaligned position of the bogie on straight track following a curve. A verification of the ADAMS/Rail simulation results by conducting specific on-track tests. • A comprehensive cost-benefit analysis.

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