Fibre optic sensors (FOS) are a promising technology for distributed health monitoring of large infrastruc- ture such as railways, bridges and pipelines. Depending on the employed light scattering measurement technique, FOS can measure strain, temperature and acceleration over tens of kilometres along the fibre length with spatial resolutions as small as five centimetre intervals. The challenge for widespread use of FOS for large infrastructure monitoring is the installation process, which is difficult, labour intensive, and time consuming. For accurate measurements, the fibre optic cable must be correctly positioned and fully bonded to the infrastructure surface with adhesive over long distances.
The research was motivated by the need to reduce the installation time and cost while achieving the installation quality requirements. A design consisting of a mobile robotic platform that travels on rails while automatically feeding and bonding fibre onto a dispensed bead of adhesive on the rail surface was developed. A prototype of the system was built and tested in the field, together with a vision-based system for monitoring the installation quality. Results from a 10 m long automatic installation test at a rail track showed good performance for the developed automatic fibre feed control and vision-based monitoring functionalities.
Acknowledgements
This project was supported in part by the National Research Council of Canada’s Artificial Intelligence for Logistics Program. The team would like to thank Emily Bugeja for helping develop the control system hardware for the robot prototype; Merrina Zhang, Taufiq Rahman, Alireza Roghani and Rob Caldwell from the National Research Council of Canada for many useful discussions; and VIA Rail Canada and the City of Kingston (Fire and Rescue) for providing access to rails for field testing the robot prototype.