Case Study – Huddersfield Uni – Network Rail - Tribosonics
Mission
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Network Rail were looking for a more reliable track lubrication measurement system that would allow monitoring of the presence of lubrication between the train wheel and track, without using manual, on-track working.   

In partnership with The University of Huddersfield, Tribosonics developed a solution using ultrasonic sensing technology. This automated vehicle mounted system communicates any areas where the lubricant is missing, thereby reducing maintenance costs and improving railway efficiency and safety. 

 

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The issue:

Network Rail apply lubrication between the train wheel and rail, primarily to reduce wear in curves and therefore increasing the time between maintenance periods. This results in increased asset life and reduced vehicle and track maintenance costs. Applying sufficient lubrication also has several secondary benefits, including reduced noise and fuel consumption. 

Currently, the only way to inspect whether lubrication is present is via manual inspections, which creates several challenges:

  • Inefficient processes
  • High costs
  • Safety implications due to on-track working
  • Periods of time when grease may not be present

Our Objective

The aim was to provide an automated vehicle-mounted system that will communicate to Network Rail the presence of lubrication, and critically, locations where it is absent, thereby reducing maintenance costs and improving railway safety.

What we did

Working with the University of Huddersfield’s award-winning Institute of Railway Research (IRR), we embedded ultrasonic sensors into the wheel to continually monitor lubrication presence and therefore allow preventative measures to be applied before problems arise. 

We developed the detection system and related hardware, with testing and sensor development carried out using the IRR’s HAROLD full-scale bogie test facility. This allowed us to process the data  to be able to easily identify where lubricant is missing using a complex machine learning algorithm.

Tests replicating real‑world conditions were also carried out using axle loads of up to 15 tons and a target operating speed of 125 mph.

The Impact

  • Proven technology that will detect the presence of lubrication in the gauge corner. 
  • Allows for automated processes for trackside maintenance
  • Identifies where lubrication is ineffective therefore resulting in high wear of the wheel and track if not rectified
  • High success rate of the detection of gauge corner lubrication

How we did it

We designed & manufactured a supportive mechanism that allowed us to stream ultrasonic data at speeds of up to 125mph. Using machine learning based algorithms, a successful detection of gauge corner lubrication condition across all train operating conditions tested.

In collaboration with the University of Huddersfield, we used their HAROLD test rig to enable the successful implementation of our sensing technology for this application.

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Our Mission Global problem We use innovative and novel approaches to ultrasonic sensors to measure and monitor a wide range of engineering applications including film thickness, wear, contact pressure, load, corrosion, material quality etc.

Global Problem