Autonomous train wheel damage detection using advanced deep learning

AECOM的大部分业务和全球专家网络专注于为运输和货运铁路系统提供解决方案。在铁路运输系统中，一个主要的目标是在最初阶段准确地检测车轮损坏，以防止灾难性的故障。车轮缺陷的类型很多，但有三种不同类型的主要损坏，即车轮平坦、粗糙和不圆度。现有的一些方法基于使用接触式传感器的振动测量来检测这些主要损伤类型中的任何一种。然而，这些传统的方法成本很高，而且只能检测到一种类型的损伤，因为它们不能准确地检测和识别所有主要的车轮损伤类型。此外，很难确认收集的数据是否真的表明损坏，而不是传感器故障、系统故障、噪声信号或这些信号的组合，并要求传感器系统和车轮亲自示意。由于恶劣的环境条件，这些感官系统故障在加拿大特别普遍。在这个项目中，申请者和AECOM希望在申请者以前使用高级深度学习进行结构损伤检测方面的成就的基础上，开发一种使用计算机视觉和高级人工智能(即深度学习)的新的自主车轮损伤检测方法。为了检测主要的车轮损坏，将使用摄像头和更快的区域卷积神经网络(FasterR-CNN)。为了减少监测和计算成本，并便于损伤检测系统的维护，传感器系统(摄像头)将安装在钢轨上，而不是每节轨道车，以测量其振动水平，从而检测损伤。该系统将极大地降低监测成本，提高车轮损伤监测系统的可靠性，从而提高现有铁路运输系统的可持续性。这一新的自主系统还将作为工业界和学术界开发更先进的损伤检测系统的模板。

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.