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

目前对铁路轨道缺陷的检查使用的是英国铁路网的四个超声波检测仪（utu），每年750个班次，遍及英国64,000英里的轨道网络。由于铁路轨道检查的限速为每小时30英里，utu无法满足客户的高需求和不断增加的容量。在英国，每天有480万人乘坐火车出行。在同一时间内，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，同时减少数千辆卡车的道路，并有助于减少温室气体排放。一个横跨英国的无风险铁路网络对于英国铁路网公司的长期规划过程战略及其在控制期6及以后运行一条安全、可靠、高效和不断发展的铁路的愿景至关重要。未被发现的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，火车延误给乘客造成了360万小时的损失，而2016/17年度，乘客向运营商索赔的服务中断费用超过7200万英镑。随着乘客对铁路运输需求的不断增长，需要提供高速（即每小时60英里以上）高分辨率、铁路轨道检查和实时数据分析的商业解决方案。一个商业解决方案，能够实现全英国网络覆盖。该RAPPID项目旨在解决英国铁路网面临的挑战，包括快速高速高分辨率铁路轨道缺陷识别、数据整理和分析、实现实时预测分析以及英国和全球铁路轨道的预测性维护。RAPPID项目基于虚拟源孔径无损检测技术的新应用，结合人工智能和深度学习方法，可以通过使用下一代相控阵超声检测硬件对轨道数据进行实时数据处理和分析。

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.