Real-time AI enabled rail track inspection and analysis [RAPPID] - Resilience Fund Application

实时人工智能支持的铁轨检查和分析 [RAPPID] - 弹性基金申请

• 批准号: 10024802
• 金额: $ 5.52万
• 依托单位: MONIRAIL LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10024802
• 关键词: Real; time; AI; enabled; rail

Current inspection of rail track defects utilises Network Rail's four Ultrasonic Testing Units (UTUs) that traverse the UK network, 64,000 miles of track, in 750 shifts per year. With a limitation of 30 miles per hour for rail track inspection, UTUs cannot meet the high demand and increased capacity of customers.Every day, 4.8 million people travel by train in Britain. Around 200,000 tonnes of freight and goods are transported by rail in that same time frame, supporting businesses and consumers, productivity, and economic growth whilst taking thousands of lorries off the road, and helping in the reduction of greenhouse gasses.A risk-free network of rail tracks across the UK is pivotal to Network Rail's long-term planning process strategy and its vision for running a safe, reliable, efficient and growing railway, in Control Period 6 and beyond. Undiscovered rail track defects lead to asset failure, unscheduled maintenance, timetable delays, accidents, and fatalities. Train delays cost passengers 3.6 million hours in 2016, whilst over £72M was claimed by passengers from operators for service disruptions in 2016/17\. With the growing demand on rail transport by passengers, there is need for commercial solutions that offers high-speed (i.e. above 60 miles per hour) high resolution, rail track inspection, and data analysis in real-time. A commercial solution with the capacity to enable UK network-wide coverage.This RAPPID project seeks to address the challenges that the UK rail network faces regarding rapid high-speed high-resolution identification of rail track defects, data collation and analysis, enabling real-time predictive analysis, and predictive maintenance of rail tracks across the UK network and globally.The RAPPID project is based on the novel use of Virtual Source Aperture non-destruction testing techniques in combination with artificial intelligence and deep-learning methodologies that enable real time data processing and analysis of rail track data derived via use of next generation phased-array ultrasonic testing hardware.

目前，铁路轨道缺陷的检测利用网络铁路的四个超声波检测单元（UTU），穿越英国网络，64,000英里的轨道，每年750班。由于铁路轨道检查的限制为每小时30英里，UTU无法满足客户的高需求和增加的容量，每天有480万人乘坐火车在英国旅行。在同一时间段内，约有20万吨货物和货物通过铁路运输，支持企业和消费者，生产力和经济增长，同时使数千辆卡车离开公路，并帮助减少温室气体。高效和不断增长的铁路，在控制期6及以后。未被发现的铁路轨道缺陷会导致资产故障、计划外维护、时间表延误、事故和死亡。2016年，列车延误使乘客损失了360万小时，而2016/17年度乘客因服务中断向运营商索赔超过7200万英镑。随着乘客对铁路运输的需求不断增长，需要提供高速（即每小时60英里以上）高分辨率、铁路轨道检测和实时数据分析的商业解决方案。一个商业解决方案，能够实现英国全网络覆盖。这个RAPPID项目旨在解决英国铁路网络面临的挑战，包括快速高速高分辨率识别铁路轨道缺陷，数据整理和分析，实现实时预测分析，RAPPID项目基于虚拟源孔径的新用途，非破坏性测试技术与人工智能和深度学习方法相结合，能够对通过使用下一代相控阵超声波测试硬件获得的轨道数据进行真实的实时数据处理和分析。