Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

A major portion of AECOM's business and global network of experts is focused on delivering solutions inx000D
transit and freight rail systems. In railway transportation systems, one major aim is to accurately detect trainx000D
wheel damage in the initial stages in order to prevent catastrophic failures. There are many types of wheelx000D
defects, but there are three different types of major damage that are wheel flat, roughness, andx000D
out-of-roundness. There are some existing approaches based on vibration measurements using contact sensorsx000D
to detect any one of these major damage types. However, these traditional methods are costly and can detectx000D
only one type of damage, because they are inaccurate to detect and identify all of the major train wheel damagex000D
types. Moreover, it is difficult to confirm that the collected data actually indicates damage rather than sensoryx000D
system malfunction, noisy signals, or a combination of these, and requires that sensing systems and wheels bex000D
checked in person. These sensory system malfunctions are particularly prevalent in Canada because of harshx000D
environmental conditions. In this project, the applicant and AECOM want to develop a new autonomous trainx000D
wheel damage detection method using computer-vision and advanced artificial intelligence (i.e., deep learning)x000D
based on the applicant's previous achievements in structural damage detection using advanced deep learning. Inx000D
order to detect major wheel damage, a camera and faster regional-convolutional neural network (Fasterx000D
R-CNN) will be used. In order to reduce monitoring and computational cost and easy maintenance of thex000D
damage detection system, the sensor system (camera) will be attached to rails instead of each rail car tox000D
measure their vibration levels and thereby detect damage. This system will drastically reduce the cost ofx000D
monitoring and improve the reliability of wheel damage monitoring systems to improve the sustainability ofx000D
existing railway transportation systems. This new autonomous system will also serve as a template for industryx000D
and academia in the development of more advanced damage detection system.