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

Lead Participant: Rcm2 Limited

Abstract

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.

**Predictive maintenance** uses data and models of the rail track and its condition to estimate remaining useful life and target maintenance where it is needed to reduce unnecessary maintenance prevent unplanned reactive maintenance in case of failure. The savings can be huge. Network Rail have identified that reaching world-class predictive, risk-based maintenance strategies could deliver the following benefits:

* **25-35%** **reduction** in maintenance costs
* **70%+** **reduction** in the number of service failures
* **35-45% reduction** in down time following failure
* **20%+ increase** in workforce productivity
* 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 obtained 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.

This project will develop a novel automated system for 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. Central to the design of the sensor system are usability and low-cost. OptRail 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.

Lead Participant

Project Cost

Grant Offer

Rcm2 Limited, Esher £418,644 £ 293,051
 

Participant

Yeltech Ltd, Guildford £305,582 £ 213,907
Brunel University London, Uxbridge £248,475 £ 248,475
Surrey Advanced Control Limited, Surrey £120,883 £ 84,618
The Welding Institute £121,553 £ 121,553

Publications

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