Track and Ride Condition Correlation for Optimised Rail Maintenance

通过测量列车本体和运行装置的加速度数据，定期、经济有效地收集准确的轨道几何形状和状态信息，仍然是基于状态的铁路轨道维护（CBM）方法的一个关键障碍。采用CBM方法每年可以节省100亿英镑的铁路维护成本。铁路轨道和车辆的维护是昂贵的，依赖于定期和被动的维护，经常造成延误。在19/20年，网络铁路（NR）每年花费13.95亿英镑用于轨道维护；轨道故障限速造成2460453分钟的延误。英国国家铁路公司使用7列检查列车，每列火车的建造成本为800万英镑，运营成本为550万英镑/年，轨道每4-26周检查一次，CBM方法的频率不足。MoniRail的解决方案直接解决了这一挑战，它是一种可改装的低成本传感器系统，将安装在两列欧洲之星列车上，并在8个月的时间里从运行的列车上收集数据，并使用数据分析来寻找数据中的相关性和/或趋势，以识别正在发展的轨道和车辆故障，并确定潜在的路线原因。预测算法的发展将为工程师提供早期发现和预测故障的能力，从而实现更有效的维护计划。该解决方案可以节省数百万美元的维护成本，并减少乘客的延误。

Regular, cost-effective collection of accurate track geometry and condition information by measurement of acceleration data from the train body and running-gear remains a key barrier to condition-based maintenance (CBM) approaches for rail-track maintenance. Adoption of CBM approaches could save \>£100M/yr in rail maintenance costs.Rail-track and vehicle maintenance is costly relying on scheduled and reactive maintenance often causing delays. In 19/20 Network Rail (NR) spent £1.395Bn/yr on track maintenance; and track fault speed-restrictions caused 2,460,453 delay minutes.NR use 7 inspection trains, each costing \>£8m to build and £5.5m/yr to operate, track is inspected every 4-26 weeks, insufficient frequency for CBM approaches.MoniRail's solution directly addresses the challenge and is a retrofittable, low-cost sensor systems will be fitted two Eurostar trains and data collected during an 8 month period from the trains running in-service and using data-analytics seek correlation and/or trends in the data to identify developing track and vehicle faults and identify underlying route causes for rough-rides. Development of predictive algorithms will provide engineers with early sight and prediction of failure to enable more effective planning of maintenance. The solution has the potential millions in maintenance costs and reduce delays for passengers.