Project Summary

This collaboration between Fishbone and MoniRail, to deliver the FWCOMS project, is focussed on the application of remote condition monitoring approaches for freight wagons to identify and monitor both train and track faults to enable the application of predictive maintenance approaches for both freight vehicles and track.

Project Achievements

Activities • Non mainline testing trials took place, alongside development of data analytics and algorithms that can be used to train machine learning models, enabling the prediction of bogie or wagon component failure. • Testing was conducted on two VTG owned hopper type wagons with varying mileage on a heritage railway track, to baseline vibration signatures of the wagons. • Pilot signal processing and machine learning exercises were conducted to produce analytics for the vibration datasets.

Conclusions

• Using the acquired baseline vibration data from the various wagons, the project results demonstratedthe ability to assess component health. • This can be used to enable the transition to on condition maintenanceof wagon components, allowing us to avoid vehicle down-time for unnecessary maintenance. • By having an accurate real-time reading of the vehicle’s health, we can reduce safety incidents on the railway, such as vehicle failures or de-railing.

Next Steps

• Seeking further funding for mainline testing trials on hopper wagons in early 2023. • Train simulations to test specific fault scenarios. • Seeded fault testing at Perk Rail Heritage Railway. Further collaboration between Fishbone and MoniRail. • FWCOMS is designed to be modular. This makes the system easily transferable to different wagon types. • Proven concept shows there is the potential to fit this system across the UK rail network.