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Asset Care Analytics: Predictive Condition Based Track Asset Management Planning Using Track Geometry and Rail Wear Condition Data

机译:资产服务分析:使用预测几何条件和轨道磨损状况数据进行基于预测条件的轨道资产管理计划

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Railroad operational performance is an essential driver to ensure the success of business strategy. Asset intensive businesses usually rely on distinctly different asset groups to be concurrently available to support operations delivering on-time scheduled service for customers in a safe and reliable manner. Maintenance management and planning for the large areas over which it requires to deliver services to customers, is even a greater challenge. Railroad's decentralized nature of networks to be maintained and the limited resources available (the need to do more with less) to effectively manage the asset health performance and its related risks increases the complexities. The advances in technology, joined with vast amounts of condition diagnostic data and data analytics approaches are improving our asset management capabilities and lead to proactively manage asset safety, performance and costs. This paper describes the approach and building blocks developed to support predictive condition based track asset management planning using asset management data such as track geometry and rail wear data. It shows the value of condition analytics that enable proactive asset care plans that improve strategic, tactical and operational management objectives with a focus on tamping and rail replacement plans.
机译:铁路运营绩效是确保业务战略成功的重要驱动力。资产密集型企业通常依赖于截然不同的资产组来同时可用,以支持以安全可靠的方式为客户提供按时计划服务的运营。维护管理和为向客户提供服务所需的大范围区域进行规划,甚至是更大的挑战。要保持铁路网络的分散性,以及有效管理资产运行状况及其相关风险的可用资源有限(需要用更少的资源做更多的事),增加了复杂性。技术的进步,加上大量的状态诊断数据和数据分析方法,正在改善我们的资产管理能力,并能够主动管理资产安全性,性能和成本。本文介绍了为支持使用资产几何数据和轨道磨损数据等资产管理数据来支持基于预测条件的轨道资产管理计划而开发的方法和构造块。它显示了条件分析的价值,这些条件使主动资产护理计划能够改善战略,战术和运营管理目标,并着重于夯实和铁路更换计划。

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