Life cycle management of railway tracks including AI-based predictive maintenance using digital product passports

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The UK Government highlights the importance of its manufacturing industry by labelling it as ‘crucial to the country's future financial stability’. In 2022, the output of 133,000 UK manufacturers (employing 2.7m people) reached £6.7tr, ranking 8th in the world. However, unplanned downtime absorbs 11% of annual production time, costing £180bn. Initial studies indicate that moving from ‘Reactive’ and ‘Preventive’ to ‘AI-based Predictive Maintenance’ (PdM) reduces these costs by 30%, breakdowns by 75%, and downtime by 45%. Large companies develop PdM mostly in-house, thereby taking on a more bespoke nature. For the UK SME manufacturing industry, predictive maintenance is in its infancy, as early investigation indicates that, whilst digitalisation is making clear inroads, maintenance is still very much reactive, and often manual. This project leverages cutting-edge AI technology to build a scalable Predictive Maintenance solution for the SME market, thereby helping it become more mainstream. The future trends in manufacturing are clearly towards digitalisation. Manufacturers are highlighting that the downtime is getting much more costly, and the crucial role of predictive maintenance in reducing costs and boosting productivity. Early industry research and interviews with potential customers indicate that the industry is cost conscious and open towards financially viable new ideas. By predicting maintenance needs accurately and allocating resources where they're most needed, our project is ushering in a new era of factory efficiency, moving away from rigid schedules to data-driven decision-making, ensuring operations run smoothly, cost-effectively, and with fewer disruptions.We propose to build a generic AI-based Predictive Maintenance product for the SME market. A key challenge of building accurate Predictive Maintenance technology is the lack of failure and breakdown data, as machines or manufacturing lines are frequently repaired before failure. Preventive maintenance considers a machine as a “stand-alone” entity in a limited environment. It does not consider the complex interaction and evolution mechanism of structural and physical attributes in a life environment. To best address these issues, our vision is to link AI based Predictive Maintenance with the concept of Digital Twins at a later stage. The ultimate goal of our holistic approach will potentially lead to the Next Generation Manufacturing Plants where health and safety, inventory and facilities management can all be more automated, sustainable, and efficient, thereby improving product quality, reducing waste and enhancing labour conditions, whilst keeping manufacturing in the UK.