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Predictive maintenance of rail infrastructure

OptRail: Multi-sensor condition monitoring for predictive maintenance of rail infrastructure using optical fibre sensors


Maintenance of rail infrastructure is a major cost to the rail industry, costing over £1billion p.a. in the UK and representing 18% of Network Rail’s expenditure. It is also a major source of network disruption from both planned and unplanned maintenance operations. Rail usage and demand for rail services is increasing rapidly, placing more traffic on the rails and increasing requirements for maintenance.

Network Rail have identified that reaching world-class predictive, risk-based maintenance strategies could deliver reduction in maintenance costs, number of service failures and down time following failure and more importantly fewer unplanned, reactive interventions delivering enhanced workforce safety.

Implementation of these strategies requires accurate, detailed and up to date knowledge of the state and condition of the railway infrastructure. This cannot be realised using traditional manual inspection and new technologies for monitoring track and infrastructure condition, and automating data acquisition, analysis and maintenance planning will be essential to deliver these benefits.


OptRail will develop a novel automated system for maintenance planning of track maintenance, using a unique combination of optical fibre sensors developed for use in harsh environments encountered in the oil and gas industry, coupled with new generation Internet of Things (IoT) communications technology and artificial intelligence to provide automated decision support tools for optimisation of maintenance programmes based on continuous real-time monitoring of track condition.

The system will complement and interface with existing digital inspection technologies such as measurement trains, filling a need for continuous, real-time monitoring and model-based prognostics and optimisation. It will provide distributed sensing of the stress state of the track, which cannot be measured by point inspections. This will allow early identification of potential track buckling sites, allowing timely preventative maintenance.

Central to the design of the sensor system are usability and low-cost. It will be developed as a modular system with the sensor elements and interfaces built into a robust package that can be reliably bonded to tracks or embedded in structures with minimum effort and downtime.


With rails heavily trafficked, inspection and maintenance cause significant disruption as they are currently only fully monitored, through disruptive methods. OptRail will offer automated and precise track maintenance requirement information through fibre optic monitoring solution allowing for bespoke timely intervention. By facilitating only targeted and necessary interventions, OptRail reduces avoidable closured hence minimising disruption to train services. This will eliminate inconvenience for rail users (cargo, passengers and operators) boosting rail’s contribution to enhancing mobility of people and goods in support of economic growth. OptRail will also make a direct and proportionate reduction of the carbon footprint of such closures in alternative transportation sourcing.

Project Partners

  • RCM2 Ltd.
  • Brunel University London
  • Surrey Advanced Control Ltd.
  • TWI Ltd.
  • Yeltech Ltd.

Meet the Principal Investigator(s) for the project

Professor Tat-Hean Gan
Professor Tat-Hean Gan - Professional Qualifications CEng. IntPE (UK), Eur Ing BEng (Hons) Electrical and Electronics Engg (Uni of Nottingham) MSc in Advanced Mechanical Engineering (University of Warwick) MBA in International Business (University of Birmingham) PhD in Engineering (University of Warwick) Languages English, Malaysian, Mandarin, Cantonese Professional Bodies Fellow of the British Institute of NDT Fellow of the Institute of Engineering and Technology Tat-Hean Gan has 10 years of experience in Non-Destructive Testing (NDT), Structural Health Monitoring (SHM) and Condition Monitoring of rotating machineries in various industries namely nuclear, renewable energy (eg Wind, Wave ad Tidal), Oil and Gas, Petrochemical, Construction and Infrastructure, Aerospace and Automotive. He is the Director of BIC, leading activities varying from Research and development to commercialisation in the areas of novel technique development, sensor applications, signal and image processing, numerical modelling and electronics hardware. His experience is also in Collaborative funding (EC FP7 and UK TSB), project management and technology commercialisation.

Related Research Group(s)

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Project last modified 12/10/2023