Autonomous train wheel damage detection using advanced deep learning

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A major portion of AECOM's business and global network of experts is focused on delivering solutions intransit and freight rail systems. In railway transportation systems, one major aim is to accurately detect trainwheel damage in the initial stages in order to prevent catastrophic failures. There are many types of wheeldefects, but there are three different types of major damage that are wheel flat, roughness, andout-of-roundness. There are some existing approaches based on vibration measurements using contact sensorsto detect any one of these major damage types. However, these traditional methods are costly and can detectonly one type of damage, because they are inaccurate to detect and identify all of the major train wheel damagetypes. Moreover, it is difficult to confirm that the collected data actually indicates damage rather than sensorysystem malfunction, noisy signals, or a combination of these, and requires that sensing systems and wheels bechecked in person. These sensory system malfunctions are particularly prevalent in Canada because of harshenvironmental conditions. In this project, the applicant and AECOM want to develop a new autonomous trainwheel damage detection method using computer-vision and advanced artificial intelligence (i.e., deep learning)based on the applicant's previous achievements in structural damage detection using advanced deep learning. Inorder to detect major wheel damage, a camera and faster regional-convolutional neural network (FasterR-CNN) will be used. In order to reduce monitoring and computational cost and easy maintenance of thedamage detection system, the sensor system (camera) will be attached to rails instead of each rail car tomeasure their vibration levels and thereby detect damage. This system will drastically reduce the cost ofmonitoring and improve the reliability of wheel damage monitoring systems to improve the sustainability ofexisting railway transportation systems. This new autonomous system will also serve as a template for industryand academia in the development of more advanced damage detection system.