One major prerequisite for an effective implementation and innovation process is the enablement and provision of a collaborative environment. A common area for multi-organisational collaboration together with a technology platform, enabling data sharing and Big Data Analytics, has been developed called ‘Testbed Railway’ with a corresponding framework ‘Railway 4.0’. Testbed Railway can be used to strengthen the railway industry's adaptability and competitiveness by developing and providing a testbed for research and innovation in the rail industry, nationally and internationally.

Globally, railway is experiencing a major technology transformation (or paradigm shift), triggered by the enhanced utilisation of digital technology. This technological transformation affects not only the technical systems, i.e. railway infrastructure and rolling stock, but also regulations, organisations, processes,and individuals. Hence, hardware, software, but also liveware (i.e. humans) are affected. Today, the digitalisation of railway is characterised by digital services. There are also a range of challenges, e.g. data acquisition,transformation, modelling, processing, visualisation, safety, security, quality, and information assurance. To deal with these challenges, the railway industry needs to define strategies, which enable a smooth transformation of the existing configuration to a digitalised system. Digital railway requires a holistic change management approach based on system-of-systems thinking and a set of appropriate technologies and methodologies. The railway digitalisation strategy should be based on systematic risk management that address aspects of, e.g., information security, traffic safety and project risk. In addition, managing changes for a digitalised railway effectively and efficiently also requires a framework for aspects such as needs finding, requirement identification, and impact of changes for individual, teams and organisation. In this work a major case studywithin the ePilot, has been performed in context of the operation and maintenance processes of the Swedish railway. Therefore, this paper aims to propose a framework for implementing innovations and driving change in a digitalised railway.

The purpose of this paper is to study and explore the essential aspects of data governance in eMaintenance that need to be considered such as data sharing and data ownership in a cross-organisational railway maintenance context. Furthermore, the paper develops and provides an approach to strategies and guidelines, which can be used to govern digital data sharing.

To fulfil this purpose, case studies of several projects where sharing of data between stakeholders in order to develop maintenance decision support, was selected as a research strategy and supported by a literature study. Empirical data were collected through interviews, workshops, document studies, and observations. An approach was developed and validated using a case study.

The proposed approach supports the understanding and establishing strategies and guidelines for data governance in a cross-organisational railway context. This can be considered as one of the enablers for information logistics for maintenance purposes where the approach can be used as a support tool in order to facilitate the development of maintenance decision support within the railway industry.

The Swedish railway is composed of several stakeholders, each one with individual objectives and strategies. Although maintenance data used by all, data sharing is not common. Due to this, the lack of data sharing, effects maintenance decision making negatively.

To support the collaboration necessary for data sharing, decision support tools and enhanced information and communication technologies are needed. Solutions have been successfully developed through research within the eMaintenance field. Research in ICT and maintenance have been conducted in the eMaintenance Lab at Luleå Railway Research Center (JVTC) at Luleå University of Technology, for more than a decade.

Condition based maintenance is enabled by implementing eMaintenance research results in the ePilot-project, in order to create a collaboration platform; which can transform the Swedish fragmented rail industry to an integrated system, from an organizational and technical point of view. Today, 25 sub-projects has been completed covering solutions e.g. system for predicting degradation of wheel and rail using data from way-side and mobile monitoring equipment and new inspection techniques.

The paper aims to describe some of the key factors that might enable or hinder the collaboration, sharing of data and implementation of good results, jointly creating the best prerequisites for the operation and maintenance of the railway system.

The goal of the project is to improve punctuality and minimize disruption in rail services. The project is an industry collaboration between JVTC, the Swedish Transport Administration, railway actors and innovators in Sweden.

In 116 thermographic measurements of 66 healthy male participants, 44 of whom were measured at least twice, hand skin temperature distributions before and after a cold stress test (CST) were examined to identify any typical characteristics of hand skin rewarming. On each hand, measurements from 18 regions of interest recorded every 10 s were used to calculate the surface average temperature. Temperatures at baseline (Tb), directly after cooling (Tc), and after 15 min of rewarming (Tf) were used for comparison and the averages of each finger, palm, and hand were analyzed. Using fits of normal distribution for the measured data, final hand skin temperatures were divided into two groups, A and B, with a calculated boundary at 25.4 °C. Digital analyses of all thermograms were performed to describe the process, and each group's rewarming patterns were observed. Group A was considered to demonstrate warm rewarming, since the whole hands reached a Tf approximately equal to the Tb. By contrast, Group B demonstrated cold rewarming and had whole hand Tf less than Tb. The predictive value of Tc was lower than that of Tb in Group A, whereas the opposite occurred in Group B. Altogether, the findings suggest different hand skin temperature rewarming patterns in healthy males.

Infrastructure managers (IMs) need to plan their maintenance work about 1½–2 years before a new train time table (TTT) comes into force to minimise the effect on traffic. Maintenance work that is planned in less than 1 year ahead of the TTT has to compete with, or need to be fitted into, operators’ applications for capacity. However, maintenance work is at times planned only a few weeks before execution, and depending on the railway line in question, a few hours during night can be available for maintenance. In addition, sudden failures in track normally require repair immediately or within a day. If rail transportation increases, it also becomes harder to find time in track for maintenance. Therefore, it is of interest to simulate maintenance tasks between trains to minimise track maintenance possession time. Such simulation can be used to: study maintenance work in TTTs with random and regular train departures; study the effect of exceeding allocated maintenance windows; and to study the effect of increase in train frequency. In this paper, Monte Carlo method is applied to simulate track maintenance between trains as a function of train frequency.