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.

Theoretical models, such as TWINS, and microphone array measurements have been widely used to gain better understanding of rolling noise. However, the array measurements are often inconsistent with the TWINS predictions and give less prominence to the rail than the theoretical models. The objectives of this thesis are to make validation work of the TWINS model for Japanese railway lines, and to explore the reason why the microphone array gives a correct estimate of sound power radiated by the rail. A comparison in terms of noise and rail vibration has been carried out for six wheel/rail conditions of Japanese railways. The TWINS predictions show good agreement with the measurements. After confirming the applicability of the TWINS model, the effects of wheel load on noise and rail vibration are investigated, and the predictions show similar trends to the measurements. The acoustic properties of a rail as measured with a microphone array have been investigated through simulations and field tests. In the simulation the rail is modelled as an array of multiple sources. Results are given for two situations: (a) the multiple sources are incoherent, which is assumed in determining sound power from a microphone array, (b) the sources are coherent, which is more representative of the rail radiation. It is found that the microphone array cannot detect a large part of the noise generated by the rail in the frequency range where free wave propagation occurs. Sound measurements were carried out to validate the radiation model of the rail by using a shaker excitation of a track. It is found that the noise is radiated from the rail at an angle to the normal when free wave propagation occurs in the rail, and that the predictions based on coherent sources show good agreement with the measurements. Sound measurements for a moving train were also performed with a microphone array. It is shown that the microphone array misses a large part of noise generated by the rail, when directed normal to the rail. This leads to an underestimation of the rail component of the noise in the array measurements.

No description available.

The exchange of information is crucial to the operation of railways; starting with the distribution of timetables, information must constantly be exchanged in any railway network. The slow evolution of the information environment within the rail industry has resulted in the existence of a diverse range of systems, only able to exchange information essential to railway operations. Were the cost of data integration reduced, then further cost reductions and improvements to customer service would follow as barriers to the adoption of other technologies are removed. The need for data integration has already been studied extensively and has been included in the UK industry's rail technical strategy however, despite it's identification as a key technique for improving integration, uptake of ontology remains limited. This thesis considers techniques to reduce barriers to the take up of ontology in the UK rail industry, and presents a case study in which these techniques are applied. Amongst the key barriers to uptake identified are a lack of software engineers with ontology experience, and the diverse information environment within the rail domain. Techniques to overcomes these barriers using software based tools are considered, and example tools produced which aid the overcoming of these barriers. The case study presented is of a degraded mode signalling system, drawing data from a range of diverse sources, integrated using an ontology. Tools created to improve data integration are employed in this commercial project, successfully combing signalling data with (simulated) train positioning data.

The word 'strategy' has represented many concepts since it originated in a military domain. Fundamentally, it is concerned with determining how best to direct systems in a changing world. However, in today's increasingly complex and vertically separated sectors, conventional top-down approaches to strategy design are becoming less applicable. The proverb 'I am because you are' encapsulates the idea that the world is not formed of independent, separate entities, but that all beings and actions are connected. This thesis puts forward a case for applying that way of thinking in a systems approach to strategy design. The proposed approach comprises three processes which are aligned with systems engineering principles, to plan, capture and communicate strategy. It is argued that strategy documents produced by following the approach would be clearer and more rigorous. To the field of strategy, this research offers a language framework for discussing strategy concepts, a new approach for designing strategy, and pragmatic guidance for strategy design exercises. In the field of systems thinking, it contributes to the challenge of translating systems engineering principles for non-traditional domains.

Air pollution from DEEEs is becoming an increased international concern, and whilst attention has been primarily focused on the automotive industry, concerns have also been raised about emissions from diesel rail vehicles. The research is designed to assess the hypothesis that diesel rolling stock severely impacts air quality at Birmingham New Street station due to the station’s enclosed nature. To assess this hypothesis, an extensive series of long term measurements were made at Birmingham New Street station. The monitoring campaign consisted of diffusion tube measurements, to measure NO2 at locations in and around the station, followed by measurements of NOx, PM, CO2 and BC at stationary and mobile sites at the platform level. The results illustrated that diesel trains serving the station elevated pollutant concentrations, particularly oxides of nitrogen. During the sampling campaign the average NO2 concentration in the centre of platform 10/11 was 407 μg/m3, approximately 10 times greater than the EU ambient air quality limit. NO exceed its WEL 35% of the time during the monitoring campaign for the same site. Furthermore, this research concludes that CO2 is not suitable as a surrogate for assessing DEEEs exposure. NO2 concentrations exceeded their relevant exposure limits, whilst CO2 did not exceed the ventilation system’s 50% speed threshold, as a result it is unlikely that harmful pollutants were being successfully exhausted from the station. The environmental analysis identified a potential trapped vortex in the West end of the station, which could have an impact on the ventilation system in place at Birmingham New Street station. It is clear that this research has been pivotal in driving a focus towards air quality with the railway industry and has prompted further research at other enclosed railway stations.

The issue of managing a large and complex railway system with continuous traffic flows and mixed train services in a safe and punctual manner is very important, especially after disruptive events. In the first part of this thesis an analysis method is introduced which allows the visualisation and measurement of the propagation of delays in the railway network. The BRaVE simulator and the University of Birmingham Single Train Simulator (STS) are also introduced and a train running estimation using STS is described. A practical single junction rescheduling problem is then defined and it investigates how different levels of delays and numbers of constraints may affect the performance of algorithms for network-wide rescheduling in terms of quality of solution and computation time. In order to deal with operational dynamics, a methodology using performance-based supervisory control is proposed to provide rescheduling decisions over a wider area through the application of different rescheduling strategies in appropriate sequences. Finally, an architecture for a real-time train rescheduling framework, based on the distributed artificial intelligence system, is designed in order to handle railway traffic in a large-scale network intelligently. A case study based on part of the East Coast Main Line is followed up to demonstrate the effectiveness of adopting supervisory control to provide the rescheduling options in the dynamic situation.

The development of the alternating current field measurement (ACFM) technique for high-speed inspection of railway wheels/track is important to ensure the integrity of railway assets. The stress and conditions at the wheel/rail contact patch, severity of rolling contact fatigue (RCF) damage and changes in microstructure and hardness with tread depth for railway wheels are investigated. This study employs experimental measurement of RCF defects in railway wheels to understand ACFM sensor signal - RCF defect relationships. The influence of sensor frequency and speed, sensor angle relative to crack angle, lift-off distance, crack propagation angle and inter-crack spacing distance are investigated. Low rather than high frequency sensors are better suited at sizing cracks in railway wheels and track. The optimum signal is obtained when the sensor is oriented parallel to the crack angle. The signal is reduced at higher speeds. The maximum change in the normalised Bx signal component of the sensor’s magnetic field is greater for closely spaced cracks (< 5 mm) and thus overestimates the defect. Inter-crack spacing distances of 15-20 mm yield estimates for crack pocket depths that are oversized by 20-36%. The inner cracks in a cluster with four defects yields higher Bₓ values than expected and thus significantly over sizes the defects at 15-20 mm inter-crack spacing distances, whereas, the sensor provides reasonable depth estimates for the outer cracks. Crack propagation angle affects the signal. Scans conducted with the probe oriented at 90° to the cluster of cracks consisting of a deep central crack surrounded by shallow cracks results in a distinct central sensor Bₓ signal that consists of a peak due to a flux leakage effect, thus, identifying the critical defect.

The author of this thesis deals with the issues of real time traffic management in junction areas and bottleneck sections on mainline railways in the event of service disturbances. A systematic methodology is proposed for modelling and solving real time train rescheduling problems in junction areas and bottleneck sections, including train re-sequencing and train re-timing. A formal mathematical model, the Junction Rescheduling Model (JRM) is proposed, based on a Mixed Integer Programming (MIP) to minimise a Weighted Average Delay (WAD). An innovative algorithm based on Differential Evolution algorithm, named DE_JRM is proposed for solving real time train rescheduling problems formulated with JRM. The performance of the algorithm DE_JRM has been evaluated with a stochastic method based on Monte-Carlo simulation methodology. The evaluation results show a good performance for both flyover and flat junctions compared with First Come First Served (FCFS) and a conventional ARS strategy. The author also extends the proposed methodology, including JRM and the algorithm DE_JRM, to model and solve real time train rescheduling problems for bottleneck sections of railway networks. Finally, an integrated system architecture for the traffic management and train control is introduced for system implementation of the proposed methodology of train rescheduling in junction areas and bottleneck sections on mainline railways.

The rail industry has focused on the improvement of maintenance through the effective use of online condition monitoring of rolling stock and rail infrastructure in order to reduce the occurrence of unexpected catastrophic failures and disruption that arises from them to an absolute minimum. The basic components comprising a railway wheelset are the wheels, axle and axle bearings. Detection of wheelset faults in a timely manner increases efficiency as it helps minimise maintenance costs and increase availability. The main aim of this project has been the development of a novel integrated online acoustic emission (AE) and vibration testing technique for the detection of wheel and axle bearing defects as early as possible and well before they result in catastrophic failure and subsequently derailment. The approach employed within this research study has been based on the combined use of accelerometers and high-frequency acoustic emission sensors mounted on the rail or axle box using magnetic hold-downs. Within the framework of this project several experiments have been carried out under laboratory conditions, as well as in the field at the Long Marston Test Track and in Cropredy on the Chiltern Railway line to London.