Multi-sensor condition monitoring for predictive maintenance of rail infrastructure using optical fibre sensors (OptRail)

使用光纤传感器进行铁路基础设施预测性维护的多传感器状态监测 (OptRail)

• 批准号: 104246
• 金额: $ 122.53万
• 依托单位: RCM2 LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=104246
• 关键词: Multi; sensor; condition; monitoring; predictive

Maintenance of rail infrastructure is a major cost to the rail industry, costing over **£1billion p.a**. in the UK and representing **18% of Network Rail's expenditure**. It is also a major source of network disruption from both planned and unplanned maintenance operations. Rail usage and demand for rail services is increasing rapidly, placing more traffic on the rails and increasing requirements for maintenance.**Predictive maintenance** uses data and models of the rail track and its condition to estimate remaining useful life and target maintenance where it is needed to reduce unnecessary maintenance prevent unplanned reactive maintenance in case of failure. The savings can be huge. Network Rail have identified that reaching world-class predictive, risk-based maintenance strategies could deliver the following benefits:* **25-35%** **reduction** in maintenance costs* **70%+** **reduction** in the number of service failures* **35-45% reduction** in down time following failure* **20%+ increase** in workforce productivity* fewer unplanned, reactive interventions delivering enhanced workforce safety.Implementation of these strategies requires accurate, detailed and up to date knowledge of the state and condition of the railway infrastructure. This cannot be obtained using traditional manual inspection and new technologies for monitoring track and infrastructure condition, and automating data acquisition, analysis and maintenance planning will be essential to deliver these benefits.This project will develop a novel automated system for planning of track maintenance, using a unique combination of **optical fibre sensors** developed for use in harsh environments encountered in the oil and gas industry, coupled with new generation **Internet of Things (IoT)** communications technology and **artificial intelligence** to provide **automated decision support** tools for optimisation of maintenance programmes based on continuous real-time monitoring of track condition. The system will complement and interface with existing **digital inspection technologies** such as measurement trains, filling a need for continuous, **real-time monitoring** and model-based prognostics and optimisation. Central to the design of the sensor system are usability and low-cost. OptRail will be developed as a modular system with the sensor elements and interfaces built into a robust package that can be reliably bonded to tracks or embedded in structures with minimum effort and downtime.

铁路基础设施的维护是铁路行业的主要成本，每年花费超过10亿英镑。在英国，占网络铁路支出的18%**。它也是计划内和计划外维护操作造成网络中断的主要原因。铁路使用量和铁路服务需求迅速增长，铁路上的交通量增加，对维护的要求也越来越高。**预测性维护 ** 使用铁路轨道及其状况的数据和模型来估计剩余使用寿命，并在需要时进行目标维护，以减少不必要的维护，防止在故障情况下进行计划外的反应性维护。节省的费用可能是巨大的。Network Rail已经确定，实现世界一流的基于风险的预测性维护策略可以带来以下好处：*25-35%* 减少 ** 维护成本 *70%+* 减少 ** 服务故障数量 * 减少35-45%** 故障后停机时间 *20%+提高 ** 劳动力生产率 * 减少计划外的反应性干预，提高劳动力安全性。这些策略的实施需要准确、详细和最新的铁路基础设施状态和状况知识。这是传统的人工检测和监测轨道和基础设施状况的新技术所无法实现的，而自动化数据采集、分析和维护规划将是实现这些好处的关键。本项目将开发一种用于轨道维护规划的新型自动化系统，使用为石油和天然气工业中遇到的恶劣环境而开发的独特的光纤传感器组合，结合新一代 ** 物联网（IoT）** 通信技术和 ** 人工智能 **，提供 ** 自动化决策支持 ** 工具，基于对轨道状况的持续实时监控优化维护计划。该系统将与现有的 ** 数字检测技术 **（如测量列车）进行补充和对接，满足对连续、** 实时监控 ** 和基于模型的自动化和优化的需求。传感器系统设计的核心是可用性和低成本。OptRail将作为一个模块化系统开发，传感器元件和接口内置在一个坚固的封装中，可以可靠地连接到轨道或嵌入结构中，只需最少的工作量和停机时间。