Rail Infrastructure 
Asset Management Summit

DIGITAL NETWORKING

• How to use data to improve train operations, centralised administrative procedures, and decision management online scheduling. Elaborate virtual conference on rail logistics cost reduction.
• Building in-house data\ analysis capabilities vs outsourcing your data operations to 3rd party IT organisations or consultants; what are the pros and cons?
• Understand how blockchain can increase digital safety in infrastructure projects & optimize sharing of data within organisations and the wider supply chain
• How to adapt internal processes to apply machine learning to unstructured data in rail to .optimise the asset management and documentation process across all digital projects. Various supply chain management and Rail Infrastructure Networking Event 2021.

The transportation industry is changing, with more and more vehicles, assets and even the most insignificant items having the ability to generate data.

SAVANA is the Digitalization platform designed to be the rational, scalable and standard way to integrate all data initiatives and future projects, fulfilling users needs, from the operators, maintainers and users perspective in terms of processes, tools, information requirements and interfaces.

This session introduces applying Digital Twin solutions to risk management in Rail. These solutions could ensure predictive maintenance applications, which can create virtual modelling of maintenance processes to prevent high-risk events reducing maintenance scheduling, and components costs for operators.

• Defining Digital Twin In Rail, What Does It Look Like & What Are The Boundaries?
• Benefits of digital twin, Infrastructure mapping for real time planning and, and predictive maintenance
• Delivering long term infrastructure components cost savings during early testing using a simulation model based approach
• The importance of data management, developing a common data structure to model data for your digital twin

DIGITAL NETWORKING

How Do We Go From Our Existing Systems To A Fully Working Digital Twin Within The next 5 Years?

• Applying Digital Twin during design testing phase to determine the functional behaviour between the existing systems and the future system to deliver component cost savings
• Building a digital map of your rail infrastructure and digital data preparation of the track to create a real-time Rail Infrastructure Virtual Business Conference of your real infrastructure
• Working with suppliers to integrate your digital twin model in the test bench to save onsite testing
• Validating Your Digital Twin To Ensure Safety And Reduce Costly Errors

With the amount of money and risk tied up in major projects, the rail industry needs more intelligence from their project analytics to help make smarter decisions.

In this session, Richard Corderoy and Martin Pocock of the Oakland Group will explore what you should be trying to do at each stage of your data journey, focusing on practical use cases, tools, and tips from the world of rail and asset management, giving you the skills and knowledge to help improve your project efficiency.

• How to additionally use LiDAR data to automatically identify asset locations
• Research & development, From creation of idea to product realization
• Training the model, change detection and reporting

DIGITAL NETWORKING

• Remote condition monitoring coverage
• Integrating existing systems and bringing in new solutions to maximise the flow of data
• Delivering for the front line, providing data when and where it is needed to allow teams to work smarter and more safely
• Insight decision support tool

• How to move from a traditional engineering mind set to deliver the full potential of data in rail operations
• What are the technical hardware, software and interface solutions driving improvements in automating predictive maintenance in rail infrastructure projects
• Standardization of open interfaces to deliver more data from connected sensors in the Infrastructure to improve fault prediction and maintenance.
• Interpreting & qualifying data: merging historical network data with current systems data to make better predictions on the state of the network
• What technology applications are operators using to perform predictive maintenance by mobile measurements

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