A knowledge-based prognostics framework for railway track geometry degradation
A knowledge-based prognostics framework for railway track geometry degradation
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DOI：
10.1016/j.ress.2018.07.004
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发表时间：
2019-01-01
影响因子：
8.1
通讯作者：
Andrews, John
中科院分区：
文献类型：
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作者：
Chiachio, Juan;Chiachio, Manuel;Andrews, John
本文提出了对基础设施资产管理建模问题的范式转变，将重点放在预测资产的未来状况上，而不是使用基于历史数据的经验建模方法。所提出的预测方法是通用的，但在本文中，它适用于铁路轨道几何形状恶化的特殊问题，因为它对整个基础设施的安全和维护成本具有重要意义。作为一项关键贡献，我们开发了一种基于知识的预测方法，该方法将轨道沉降的在线数据与基于物理的轨道退化模型融合在基于过滤的预测算法中。所提出的方法的适用性在一个案例研究中进行了论证和讨论，该案例研究使用了在诺丁汉大学（英国）进行的循环荷载下铁路轨道沉降的实验室模拟的公开数据。结果表明，所提出的方法能够在过程寿命的10%左右的模型训练周期后，对系统的剩余使用寿命提供准确的预测。
This paper proposes a paradigm shift to the problem of infrastructure asset management modelling by focusing towards forecasting the future condition of the assets instead of using empirical modelling approaches based on historical data. The proposed prognostics methodology is general but, in this paper, it is applied to the particular problem of railway track geometry deterioration due to its important implications in the safety and the maintenance costs of the overall infrastructure. As a key contribution, a knowledge-based prognostics approach is developed by fusing on-line data for track settlement with a physics-based model for track degradation within a filtering-based prognostics algorithm. The suitability of the proposed methodology is demonstrated and discussed in a case study using published data taken from a laboratory simulation of railway track settlement under cyclic loads, carried out at the University of Nottingham (UK). The results show that the proposed methodology is able to provide accurate predictions of the remaining useful life of the system after a model training period of about 10% of the process lifespan.