Automation of Tenanted Arches NDT Inspections using Robotics and Machine Learning

这个创新的项目旨在自动化检查包覆层的租赁拱门，而不需要去除包覆层，并且还可以检测拱门的表面缺陷，这是目前现有方法无法实现的。该项目将重点使用机器人设备和无损检测（NDT）技术，如探地雷达（GPR）和x射线后向散射（XBS），以建立一个准确的3D图像，显示租户拱门的整体健康状况。该提案是MTC和Network Rail之间的成功活动的延续，即租户拱门项目流，其中使用GPR和XBS技术在大伦敦地区手动扫描覆盖层的租户拱门，快速且无需移除或损坏任何覆盖层。在项目的最新阶段，可以手动收集所需的数据，然后快速生成有关包层材料背后所有相关缺陷的尺寸和位置的人类可读信息。到目前为止，所有的检查都是由操作员手动完成的，因此该提案将重点放在完全自动化的过程上，这样就会更快、更安全。拟议的项目包括四项主要活动：为机器人设备定义一个自动化平台，在不去除包层的情况下检查租用的拱门，增强数据处理技术，以提高无损检测扫描中的图像质量，部署机器学习算法，用于自动缺陷检测和3D空间可视化，并进行概念验证演示，以展示系统和传感器的集成。通过自动化检查、加强数据分析和利用先进技术，该项目旨在简化租赁拱门检查，提高收集信息的质量和数量，并改进预测性维护计划。最终，该项目将为铁路基础设施的包覆拱提供更安全、更快速、更可靠的检测解决方案。

This innovative project that aims to automate the inspection of cladded tenanted arches without the need to remove the cladding, and to also detect sub-surface defects in the arches, which is not currently possible with current methods. The project will focus on the use of robotics devices coupled with Non-Destructive Testing (NDT) techniques such as Ground Penetrating Radar (GPR) and X-ray Backscatter (XBS) to build an accurate and 3D picture of the overall health of the tenanted arches.This proposal is a continuation of the successful activities between the MTC and Network Rail, the Tenanted Arches Project stream, in which GPR and XBS technologies were utilised to manually scan cladded tenanted arches within Greater London, quickly and without the need to remove or damage any cladding. In the latest stages of the project, it was possible to manually collect the required data and then quickly produce human readable information about the size and location of all relevant defects behind the cladding material. All the inspection thus far has been completed manually by operators, so this proposal will focus on fully automating the process such that it is faster and safer.The proposed project involves four main activities: defining an automation platform for robotics devices to inspect tenanted arches without cladding removal, enhancing data processing techniques to improve image quality in the NDT scans, deploying machine learning algorithms for automated defect detection and visualisation in 3D space, and conducting a proof-of-concept demonstration to showcase the integration of systems and sensors. By automating inspections, enhancing data analysis, and leveraging advanced technologies, the project aims to streamline tenanted arch inspections, increase the quality and quantity of collected information, and improve predictive maintenance scheduling. Ultimately, the project will provide a safer, faster, and more reliable inspection solution for cladded tenanted arches in railway infrastructure.