Protection and Resilience for OLE using Computer Vision Techniques (PROLECT)

使用计算机视觉技术的 OLE 保护和弹性 (PROLECT)

• 批准号: 10039201
• 金额: $ 31.49万
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10039201
• 关键词: Protection; Resilience; OLE; using; Computer

This project will utilise Computer Vision techniques applied to existing video footage and capturing a new type of video sensor to address two main challenges which are exacerbated by weather events and can result in the railway being closed. Providing means in which these type of challenge can be predicted and prevented will help provide the railway to become more resilient to weather events and season agnostic.The following two areas will be addressed:* Extreme hot weather causes OLE wires to extend and cause the tensioners to come into contact with the ground which can reduce tension and cause damage or event de-wirement. Utilising existing video footage this project will automatically identify OLE tensioners, position and measure them and generate an live asset map with current status level. This can be utilised as part of a digital twin model and fed into systems which are able to alert maintainers to the issue so they are able to take preventative action.* Hot, cold and humid weather can also have an impact causing Corona discharge from electrical assets such as insulators. The Corona discharge is an early warning sign of potential damage and failure of the equipment and can be measured as part of a predictive maintenance regime to prioritise preventative maintenance activities. This project will install a UV Corona camera onto a measurement fleet or in-service train and enable automated data capture with real-time data transmission and processing. The results will be evaluated by experienced working groups to tune and amend the level of Corona events to ensure an optimum level of precision and recall ensuring an operationally useful tool.Both of these events can have very impacts on the railway in terms of delay, safety and customer experience. By providing tools which have the capability of preventing these from occurring the railway will have an increased resilience to the weather conditions.

该项目将利用计算机视觉技术应用于现有的视频片段和捕捉新型视频传感器，以应对两个主要挑战，这两个挑战因天气事件而加剧，可能导致铁路关闭。提供可预测和防止此类挑战的方法，将有助于使铁路对天气事件和季节不可知变得更具弹性。以下两个方面将得到解决：*极端炎热的天气会导致OLE电线伸展，并导致张紧器接触地面，从而减少张力，导致损坏或事件减损。利用现有的视频片段，该项目将自动识别OLE张紧器，定位和测量它们，并生成具有当前状态级别的实时资产地图。这可以作为数字双胞胎模型的一部分，并输入到系统中，这些系统能够提醒维护人员注意这个问题，以便他们能够采取预防措施。*炎热、寒冷和潮湿的天气也会产生影响，导致绝缘子等电气资产的电晕放电。电晕放电是设备潜在损坏和故障的早期预警信号，可以作为预测性维护制度的一部分进行测量，以确定预防性维护活动的优先顺序。该项目将在测量车队或在役列车上安装一台UV电晕相机，并实现自动数据捕获和实时数据传输和处理。结果将由经验丰富的工作组进行评估，以调整和修改日冕事件的级别，以确保最佳的精确度和召回率，以确保操作上有用的工具。这两个事件都可能在延误、安全和客户体验方面对铁路产生非常大的影响。通过提供能够防止这些情况发生的工具，铁路将具有更强的对天气条件的适应能力。