Railway utilization is increasing. In 2017-18 there were over 1.7 billion passenger journeys made on the UK railway network, more than double the number made in 1995. Therefore getting maximum performance from the existing network by streamlining maintenance is vital. Previous research has tended to model maintenance interventions as uniform length and assume no spatial dependencies. However, in reality maintenance decisions are generally taken accounting for the condition of the whole of a route section thus prioritizing the utilization of the available resources. In this research, Petri nets will be used to explore a range of techniques for modelling maintenance on a 100 mile section of UK railway. Four methodologies will be implemented and compared. Initially the system will be modelled using a single small (220 yards) section and the values for the route section extrapolated from this. The second method will model the system as 849 individual small sections, with some interactions and dependencies considered between the track sections. The third methodology will consider that maintenance will affect a group of adjacent sections and maintenance will only be performed if all sections are degraded. The final methodology will schedule work using work banks to allow the number of sections maintained during an intervention to vary.

Railway networks are complex systems, and the management of such systems is a challenging task for railway asset managers. It is their responsibility to ensure that the network delivers the highest level of performance for all stakeholders while adhering to strict safety regulations and financial constraints. Historically, reliability, availability, maintainability and safety (RAMS) analysis has been used to assess the performance and safety of railway networks. Nonetheless, there is a lack of consistency in approaches across the industry, with analysis often influenced by the key stakeholders at the time. This research demonstrates an application of an extended RAMS (ExRAMS) framework on the UK railway network. The ExRAMS framework aims to consolidate various extensions to the traditional RAMS approach into a single universal approach, which is beneficial to all stakeholders. This paper explores the data currently available within the rail industry and how these can be used to assess the ten metrics within the framework. The final part of the paper explores how the parameters within the ExRAMS framework can be used as the bases of a value analysis, which can then be used to assist with asset-management decisions.

Switches and Crossings (S&C) are a fundamental part of any railway network, they allow trains to switch between tracks and to cross over other tracks. They consist of various electrical and mechanical components to which there is a substantial maintenance cost. Failure of an S&C unit can cause significant disruption to traffic and have large financial implications. Therefore, planning their maintenance is of critical importance to railway asset managers. This research proposes an asset management framework, which models the degradation, failure, inspection and maintenance for the S&C unit. The framework comprises nine Petri net sub-models for the S&C component availability and predicts the number of maintenance interventions in a given time period. This can be used to inform maintenance decision making, with the aim of reducing the life cycle cost of the S&C.

Railway asset managers have finite resources which requires them to make strategic decisions on where, when and how the available budget will be spent on the railway, while ensuring safety limits are maintained and a high level of performance is delivered for customers. The purpose of this research is to develop a suitable framework, which can be used by railway asset managers, to quantify asset performance in a way that enables comparisons between different parts of the railway and enables the asset manager to make key decisions on how the railway can be improved. A frequently used assessment tool is RAMS (Reliability, Availability, Maintainability and Safety) analysis. In recent years RAMS analysis has been extended to include additional parameters such as: security, health, environment, economics and politics (SHEeP). This research proposes an extended RAMS framework, which considers 12 parameters in a four level hierarchy, specifically for use on railway networks by railway asset managers.

Within any railway network Switches and Crossings (S&C) are essential. They allow trains to change tracks, allowing different routes to be selected. Despite their necessity, they generally have a lower reliability than plain line track and are often subject to breakdowns due to the high number of interlinking electrical and mechanical components they contain. Due to their location such as station throats and major junctions, S&C breakdown is generally very disruptive to traffic causing significant delays. Ensuring that S&C units are maintained correctly and minimising their risk of failure, is therefore of critical importance to railway asset managers. This research uses maintenance and failure data to determine probability distributions for the degradation, failure, inspection and maintenance of nine critical components within S&C units. These distributions can then be used within an asset management framework to simulate the expected operational behaviour of an S&C unit under a given set of conditions, allowing more informed asset management decisions to be taken.