Development of a whole life cycle cost model for electrification options on the UK rail system

Projects to deliver Overhead Line Equipment (OLE) electrification on the UK rail infrastructure system presents technical challenges which the rail industry in Britain have not traditionally had to consider. Whole Life Cycle assessment provides decision makers with cost estimates for the installation phase and over the entire service life of the system, including disposal. The OLE projects face a particular problem when analysing the best option for overbridges. Much of the rail infrastructure has not traditionally had to consider overhead clearances and therefore many of the bridges are only a little taller than the rolling stock. In addition to the difficulties in assessing the Life-Cycle costs of assets that have historically been used in very limited scales, the Whole Life Cycle assessment must consider the various engineering options that are available for projects. The three competing options (bridge rebuild, track lowering, reduced clearance) are all going to have very different capital expenditure (CAPEX) and operating expenditure (OPEX) costs. This work presents a model created to predict these costs over the anticipated assessment period. The developed model predicts capital expenditures, maintenance and service disruption costs and links them to the three major assets options involved in OLE underbridges

Uncertainty of net present value calculations and the impact on applying integrated maintenance approaches to the UK rail industry

The Public performance indicator (PPI) is an important Key Performance Indicator for Network Rail and monitored carefully by the organisation and their external stakeholders. Condition monitoring is of increasing interest within network rail as a suitable method for increasing asset reliability and improving the PPI metric. As condition monitoring methods are identified each will need assessment to establish the cost and benefit. Benefit can be measured in cost savings as poor PPI performance results in fines. Within many industries Net Present Value (NPV) calculations are used to determine how quickly investments will break-even. Cost-risk is a term that is used to describe the financial impact of an unexpected event (a risk). This paper outlines a more detailed approach to calculating NPV which considers the cost-risk effect of changes of the denial of service charging rate. NPV prediction is of importance when assessing when to deploy different fault detection strategies to maintenance issues, and therefore the cost-risk of the NPV calculation should be used to support asset management decisions

Cost drivers of integrated maintenance in high-value systems

High value systems are determined by a wide structure, where operations are considered to be one structural component. Nowadays “down-time” as a major impact in the operation costs of any system. To avoid or minimize “down-time” it is essential to match the appropriate maintenance to each failure. Therefore, it is relevant to determine the cost drivers of integrated maintenance in any system, in order to minimize the overall cost. It is common to use Value Driven Maintenance (VDM) to capture the cost drivers in maintenance. VDM is a methodology which relies in four distinct areas: Asset Utilization; Resource Allocation; Control Cost and Health and Safety and Environment. Within each category it is possible to allocate different cost drivers, building a framework for each system studied. The aim of this paper is to categorize the cost drivers of rail infrastructure networks, associating them with the maintenance preformed for each case. Furthermore, analysis of which part of the track falls under each VDM category as well as the general failure causes and effects will be included in the framework presented. Finally relating the maintenance type for each effect will provide the necessary inputs towards a cost model structure. The benefit of achieving a successful model will be the optimization of the cost in integrated maintenance of the rail infrastructure

Integration of cost-risk assessment of denial of service within an intelligent maintenance system

As organisations become richer in data the function of asset management will have to increasingly use intelligent systems to control condition monitoring systems and organise maintenance. In the future the UK rail industry is anticipating having to optimize capacity by running trains closer to each other. In this situation maintenance becomes extremely problematic as within such a high-performance network a relatively minor fault will impact more trains and passengers; such denial of service causes reputational damage for the industry and causes fines to be levied against the infrastructure owner, Network Rail. Intelligent systems used to control condition monitoring systems will need to optimize for several factors; optimization for minimizing denial of service will be one such factor. With schedules anticipated to be increasingly complicated detailed estimation methods will be extremely difficult to implement. Cost prediction of maintenance activities tend to be expert driven and require extensive details, making automation of such an activity difficult. Therefore a stochastic process will be needed to approach the problem of predicting the denial of service arising from any required maintenance. Good uncertainty modelling will help to increase the confidence of estimates. This paper seeks to detail the challenges that the UK Railway industry face with regards to cost modelling of maintenance activities and outline an example of a suitable cost model for quantifying cost uncertainty. The proposed uncertainty quantification is based on historical cost data and interpretation of its statistical distributions. These estimates are then integrated in a cost model to obtain accurate uncertainty measurements of outputs through Monte-Carlo simulation methods. An additional criteria of the model was that it be suitable for integration into an existing prototype integrated intelligent maintenance system. It is anticipated that applying an integrated maintenance management system will apply significant downward pressure on maintenance budgets and reduce denial of service. Accurate cost estimation is therefore of great importance if anticipated cost efficiencies are to be achieved. While the rail industry has been the focus of this work, other industries have been considered and it is anticipated that the approach will be applicable to many other organisations across several asset management intensive industrie

Challenges in cost analysis of innovative maintenance of distributed high-value assets

Condition monitoring is an increasingly important activity, but there is often little thought given to how a condition monitoring approach is going to impact the cost of operating a system. This paper seeks to detail the challenges facing such an analysis and outline the likely steps such an analysis will have to take to more completely understand the problem and provide suitable cost analysis. Adding sensors might be a relatively simple task, but those sensors come with associated cost; not only of the sensor, but of the utilities required to power them, the data gathering and processing and the eventual storage of that data for regulatory or other reasons. By adding condition monitoring sensors as a sub- system to the general system an organisation is required to perform maintenance to the new sensors sub-system. Despite these difficulties it is anticipated that for many high value assets applying condition monitoring will enable significant cost savings through elimination of maintenance activities on assets that do not need such cost and effort expended on them. Further savings should be possible through optimisation of maintenance schedules to have essential work completed at more cost efficient times

Investigating precision and accuracy of a robotic inspection and repair system

Robot integration in railway maintenance steps a prominent pavement in high-efficient and low-cost job execution for the infrastructure management. To achieve practical and diverse inspection and repair railway job, a robot manipulator on a locomotive platform is one of the best options. A lot of research has been conducted to find the accuracy and precision of industrial robotic manipulator where the manipulator base is fixed. This paper initiates an exploration of the accuracy and precision of a Robotic Inspection and Repair System (RIRS), which is a novel robotic railway maintenance system integrated with an industrial manipulator (UR10e) with 6 degree-of-freedom, mounting on an Unmanned Ground Vehicle (UGV) (Warthog) and specially designed trolley. In this research, a mimic track visual inspection test using QR code detection is adopted and implemented by an arm-mounted monocular camera. Then a sequential pose moves with multiple payload weights on the manipulator end has been performed as a performance measurement of repair jobs using a vision-based position tracking algorithm. The measurement results demonstrate that RIRS can maintain accurate and consistent performance in both defect position inspection and repair moves with diverse payloads. For inspection the positional error was only 0.27% while for repair moves the end-effector can reach the same position within 1mm. This research establishes a foundation for system command & control development and supporting more practical railway jobs deployment towards full autonomy for RIRS in the future

Cost data visualisation

Decision making using the methodologies and analysis generated by the cost engineering function is widely considered good practice across industry, as a way to support both technical engineering decisions and fundamental business decisions. One persistent challenge for the professional cost engineer is to present cost data and information to decision makers and a mix of audiences. Data visualisation is therefore an important element to ensure that data is presented in a clear, effective and convenient format to ensure sufficient insights can be gathered. This work explores different data presentation and visualisation approaches. This review highlighted this topic as a research gap that this paper is novel in addressing. The review findings are further explored through a series of semi-structured interviews with experts in relevant fields to establish effective data visualisation methods, along with the challenges associated with presenting cost data to a variety of audiences. Chart embellishments are one explored area of potential to increase the engagement and understanding of visualisations

An autonomous system for maintenance scheduling data-rich complex infrastructure:Fusing the railways’ condition, planning and cost

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation

Maternal health interventions in resource limited countries: a systematic review of packages, impacts and factors for change

The burden of maternal mortality in resource limited countries is still huge despite being at the top of the global public health agenda for over the last 20 years. We systematically reviewed the impacts of interventions on maternal health and factors for change in these countries. A systematic review was carried out using the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles published in the English language reporting on implementation of interventions, their impacts and underlying factors for maternal health in resource limited countries in the past 23 years were searched from PubMed, Popline, African Index Medicus, internet sources including reproductive health gateway and Google, hand-searching, reference lists and grey literature. Out of a total of 5084 articles resulting from the search only 58 qualified for systematic review. Programs integrating multiple interventions were more likely to have significant positive impacts on maternal outcomes. Training in emergency obstetric care (EmOC), placement of care providers, refurbishment of existing health facility infrastructure and improved supply of drugs, consumables and equipment for obstetric care were the most frequent interventions integrated in 52%-65% of all 54 reviewed programs. Statistically significant reduction of maternal mortality ratio and case fatality rate were reported in 55% and 40% of the programs respectively. Births in EmOC facilities and caesarean section rates increased significantly in 71%-75% of programs using these indicators. Insufficient implementation of evidence-based interventions in resources limited countries was closely linked to a lack of national resources, leadership skills and end-users factors. This article presents a list of evidenced-based packages of interventions for maternal health, their impacts and factors for change in resource limited countries. It indicates that no single magic bullet intervention exists for reduction of maternal mortality and that all interventional programs should be integrated in order to bring significant changes. State leaders and key actors in the health sectors in these countries and the international community are proposed to translate the lessons learnt into actions and intensify efforts in order to achieve the goals set for maternal health

The retrospective analysis of Antarctic tracking data project

The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s. The data are publicly available through biodiversity.aq and the Ocean Biogeographic Information System. The archive includes tracking data from over 70 contributors across 12 national Antarctic programs, and includes data from 17 predator species, 4060 individual animals, and over 2.9 million observed locations