DepotMATE - Multi-sensor Automated Train Examination

DepotMATE - 多传感器自动列车检查

• 批准号: 10088085
• 金额: $ 50.19万
• 依托单位: ONE BIG CIRCLE LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10088085
• 关键词: DepotMATE; Multi; sensor; Automated; Train

This project and the delivery of the DepotMATE (Multi-sensor Automated Train Examination) solution aims to meet the safety and efficiency challenges associated with depot-based rolling stock inspections. The diverse and congested UK rail network relies on efficient depot operations to ensure passenger, freight, light and heavy rail services run safely and on time, with high-performing assets. With the vast volume of inspection scheduling required to ensure assets are operating effectively, traditional manual examinations require a large amount resource, coming at a cost to both the operator and passenger where services are impacted by depot delays. This project collates a host of technologies incorporating sensor-fusion, advanced Machine Learning, and One Big Circle's industry-leading Intelligent Video expertise to enable operators to remotely examine a multitude of asset conditions in a reduced timeframe.The DepotMATE system will incorporate a multi-sensory lightweight inspection system, deployed to depots or sidings and positioned to capture passing rolling stock vehicles. Combining thermographic, acoustic, and Forwards Facing Video cameras and sensors and additional transmission of train-borne data via edge-processing, the DepotMATE will simultaneously capture a plethora of critical data as rolling stock vehicles pass by. A 'plug & play' design will enable partnering operators to configure sensors for their specific units, effectively targeting each operator's examination requirements and necessary inspection zones. DepotMATE will arm operators with automated monitoring data across a breadth of rolling stock, to assure cost-effective delegation of resources when managing depot operations, and successful delivery of proactive maintenance, helping to reduce costly reactive repairs and optimise the maintenance of rolling stock assets.DepotMATE will assist further automation across rolling stock inspection activities through the application of Machine Learning models, to automatically detect visibly apparent faults/vehicle contamination, exceedingly hot components, and acoustic emissions which may signify defective wheels or braking systems.Data will be accessible in extremely low-latency online, via One Big Circle's Automatic Intelligent Video Review (AIVR) platform, to enable depot operatives, control units, and fleet managers to remotely inspect each vehicle within their depot. Aspects of vehicle cleanliness, asset status, and vehicle allocation within a depot will be presented to the project's partnering operators at the touch of a button, massively reducing requirements for personnel to walk trackside whilst empowering users to inform predictive maintenance decisions.

该项目以及depotate（多传感器自动列车检测）解决方案的交付旨在满足与基于车辆段的车辆检测相关的安全和效率挑战。多样化和拥挤的英国铁路网络依赖于高效的车站运营，以确保客运、货运、轻型和重型铁路服务的安全、准时运行，并拥有高性能的资产。为了确保资产的有效运行，需要进行大量的检查调度，而传统的人工检查需要大量的资源，这对运营商和乘客来说都是一种成本，因为车站延误会影响服务。该项目整合了一系列技术，包括传感器融合、先进的机器学习和One Big Circle行业领先的智能视频专业知识，使运营商能够在较短的时间内远程检查多种资产状况。DepotMATE系统将集成一个多感官轻型检测系统，部署在车辆段或侧边，定位捕捉过往的铁路车辆。DepotMATE结合了热成像、声学、前向视频摄像机和传感器，以及通过边缘处理的额外列车数据传输，可以在轨道车辆经过时同时捕获大量关键数据。“即插即用”设计将使合作作业者能够为其特定装置配置传感器，有效地针对每个作业者的检查要求和必要的检查区域。depotate将为运营商提供广泛的铁路车辆自动监控数据，确保在管理车辆段运营时具有成本效益的资源分配，并成功交付主动维护，帮助减少成本高昂的被动维修，优化铁路车辆资产的维护。depotate将通过应用机器学习模型，协助车辆检测活动进一步实现自动化，自动检测明显的故障/车辆污染、过热部件以及可能表明车轮或制动系统有缺陷的声发射。通过One Big Circle的自动智能视频审查（AIVR）平台，数据将以极低的延迟在线访问，使仓库操作员、控制单元和车队经理能够远程检查仓库内的每辆车。车辆段内的车辆清洁度、资产状态和车辆分配等信息将通过一个按钮呈现给项目的合作运营商，这大大减少了人员在轨道旁行走的需求，同时使用户能够为预测性维护决策提供信息。