Reference number : GC-2017-Q2-04262, amendment number : , agreement type : G, recipient legal name : Cha, Young Jin (University of Manitoba)|Cha, Young Jin (Université du Manitoba), recipient country : CA, recipient province : nan, recipient city : Manitoba, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : A major portion of AECOM's business and global network of experts is focused on delivering solutions in_x000D_
transit and freight rail systems. In railway transportation systems, one major aim is to accurately detect train_x000D_
wheel damage in the initial stages in order to prevent catastrophic failures. There are many types of wheel_x000D_
defects, but there are three different types of major damage that are wheel flat, roughness, and_x000D_
out-of-roundness. There are some existing approaches based on vibration measurements using contact sensors_x000D_
to detect any one of these major damage types. However, these traditional methods are costly and can detect_x000D_
only one type of damage, because they are inaccurate to detect and identify all of the major train wheel damage_x000D_
types. Moreover, it is difficult to confirm that the collected data actually indicates damage rather than sensory_x000D_
system malfunction, noisy signals, or a combination of these, and requires that sensing systems and wheels be_x000D_
checked in person. These sensory system malfunctions are particularly prevalent in Canada because of harsh_x000D_
environmental conditions. In this project, the applicant and AECOM want to develop a new autonomous train_x000D_
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. In_x000D_
order to detect major wheel damage, a camera and faster regional-convolutional neural network (Faster_x000D_
R-CNN) will be used. In order to reduce monitoring and computational cost and easy maintenance of the_x000D_
damage detection system, the sensor system (camera) will be attached to rails instead of each rail car to_x000D_
measure their vibration levels and thereby detect damage. This system will drastically reduce the cost of_x000D_
monitoring and improve the reliability of wheel damage monitoring systems to improve the sustainability of_x000D_
existing railway transportation systems. This new autonomous system will also serve as a template for industry_x000D_
and academia in the development of more advanced damage detection system., program purpose french : A major portion of AECOM's business and global network of experts is focused on delivering solutions in_x000D_
transit and freight rail systems. In railway transportation systems, one major aim is to accurately detect train_x000D_
wheel damage in the initial stages in order to prevent catastrophic failures. There are many types of wheel_x000D_
defects, but there are three different types of major damage that are wheel flat, roughness, and_x000D_
out-of-roundness. There are some existing approaches based on vibration measurements using contact sensors_x000D_
to detect any one of these major damage types. However, these traditional methods are costly and can detect_x000D_
only one type of damage, because they are inaccurate to detect and identify all of the major train wheel damage_x000D_
types. Moreover, it is difficult to confirm that the collected data actually indicates damage rather than sensory_x000D_
system malfunction, noisy signals, or a combination of these, and requires that sensing systems and wheels be_x000D_
checked in person. These sensory system malfunctions are particularly prevalent in Canada because of harsh_x000D_
environmental conditions. In this project, the applicant and AECOM want to develop a new autonomous train_x000D_
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. In_x000D_
order to detect major wheel damage, a camera and faster regional-convolutional neural network (Faster_x000D_
R-CNN) will be used. In order to reduce monitoring and computational cost and easy maintenance of the_x000D_
damage detection system, the sensor system (camera) will be attached to rails instead of each rail car to_x000D_
measure their vibration levels and thereby detect damage. This system will drastically reduce the cost of_x000D_
monitoring and improve the reliability of wheel damage monitoring systems to improve the sustainability of_x000D_
existing railway transportation systems. This new autonomous system will also serve as a template for industry_x000D_
and academia in the development of more advanced damage detection system., agreement title english : Autonomous train wheel damage detection using advanced deep learning, agreement title french : Autonomous train wheel damage detection using advanced deep learning, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-09-20, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada