Field Data based Predictive Maintenance and Enhanced Track Design Procedure

基于现场数据的预测性维护和增强的轨道设计程序

• 批准号: LP200200915
• 负责人: Prof Buddhima Indraratna
• 金额: $ 50.18万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-09-04 至 2024-09-03
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP200200915
• 关键词: Field; Data; based; Predictive; Maintenance

Providing improved railroad design and maintenance solutions with accurate and timely assessment of track performance is vital to reduce the disruption of services, excessive maintenance costs and degradation over time. This project aims to develop a predictive maintenance model based on extensive wayside measurements to monitor and predict the nature of track instability. This will also be supplemented by mathematical modelling to corroborate large-scale laboratory observations with past and real-time field monitoring records. This project will deliver tangible strategies and tools to increase track longevity and assist decision-makers in planning and prioritising corrective maintenance to ensure improved safety and passenger comfort.

提供改进的铁路设计和维护解决方案，并对轨道性能进行准确及时的评估，对于减少服务中断、过度的维护成本和随着时间的推移而退化至关重要。该项目旨在开发一种基于广泛的轨旁测量的预测性维护模型，以监测和预测轨道不稳定性的性质。这还将辅之以数学建模，以证实大规模实验室观察与过去和实时实地监测记录。该项目将提供切实的战略和工具，以延长轨道寿命，并协助决策者规划和优先考虑纠正性维护，以确保提高安全性和乘客舒适度。