Our approach is to integrate Internet-of-Things (IoT) sensors into rail station assets for monitoring and predictive maintenance. These IoT sensors will be connected to an Artificial Intelligence-based simulation platform to virtually simulate and evaluate rail assets. The platform will use novel predictive models (i.e. deep-learning) to provide alerts of potential asset failures and suggest optimal maintenance-plan in real-time. The AI-simulation platform will also incorporate pedestrian-flow-models to provide insight into customers' behaviour and to predict how and when station services could be improved. The solution will also support operatives with in-situ asset information and maintenance procedure using Augmented-Reality (AR).
The rail sector needs to move from the 'find and fix' approach to one of 'predict and prevent'. Predictive and preventive maintenance have been identified as the top use case driving Internet of Things (IoT) market growth (Research-and-Market 2017), which is forecasted to reach £210 billion by 2020 (BCG 2017).
The UK rail network experienced over 233,000 cancelled rail journeys in 2016 (Office-of-Road-and- Rail- ORR,2016), which were mostly caused by asset failures and unscheduled maintenance. As a result, over £28 million was claimed for service disruptions in 2016. Delays due to asset failures are increasing and maintenance expenditure is not decreasing as expected (NetworkRail, 2016). Data from rail network assets and users has not been leveraged for predictive and preventive maintenance (NetworkRail, 2016).
Currently, there are no holistic solutions that leverage asset data, and novel Artificial Intelligence (AI) and Augmented Reality (AR) technologies for predictive and preventive maintenance. E.g. to identify anomalies promptly that could remain undetected until regular inspections are undertaken and to reduce maintenance costs by supporting maintenance staff.
This project will address this business need by developing an IoT-enabled Platform for Rail Assets Monitoring and Predictive Maintenance (i-RAMP). The consortium will leverage their existing connections (Network Rail, Cross Rail, London Underground, HS2) to boost this market opportunity and increase its market share by ~4%. This project will provide an operational edge to the consortium by enabling ~50% time reduction in asset tracking, which represents ~30% cost reduction in maintenance tasks. It will extend the consortium's portfolio through a Spin-out company set-up to provide high-value services.
This project is funded by