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A multivariate statistical representation of railway track irregularities using ARMA models
Vehicle System Dynamics ( IF 3.5 ) Pub Date : 2021-04-08 , DOI: 10.1080/00423114.2021.1912365
J. N. Costa 1 , J. Ambrósio 1 , D. Frey 2 , A. R. Andrade 1
Affiliation  

Railway track irregularities occur naturally from service operations and are characterised by deviations of the track relative to its nominal geometry. Since the dynamic behaviour of a rail vehicle depends on the geometry of the track, studying irregularities is essential for safety, comfort, and maintenance. Conventionally, an irregularity is synthesised from a univariate stochastic process, which is a suitable way to represent excitations occurring in one dimension. However, most three-dimensional analyses of rail vehicles require a multivariate description of the irregularities. Therefore, synthesising multiple variables using univariate processes neglects possible relationships among them. This work addresses track irregularities from a multivariate series perspective to model the spatial autocorrelation of each irregularity and their spatial correlation with other types of irregularities. Several multivariate models with different complexity levels are estimated from the irregularities of a 3.6-km long straight track segment. A comparison among models shows that they should be selected based on not only statistical information criteria, but also their ability to reproduce relevant physical characteristics. Finally, a case study shows that the vehicle response to irregularities that are synthesised using the proposed multivariate process matches the response to measured data better than irregularities synthesised using independent univariate processes.



中文翻译:

使用 ARMA 模型的铁路轨道不规则性的多元统计表示

铁路轨道不规则性在服务运营中自然发生,其特点是轨道相对于其标称几何形状的偏差。由于轨道车辆的动态行为取决于轨道的几何形状,因此研究不规则性对于安全、舒适和维护至关重要。通常,不规则性是从单变量随机过程合成的,这是表示在一维中发生的激发的合适方式。然而,大多数轨道车辆的三维分析都需要对不规则性进行多变量描述。因此,使用单变量过程合成多个变量会忽略它们之间可能存在的关系。这项工作从多变量序列的角度解决了跟踪不规则性,以对每个不规则性的空间自相关及其与其他类型的不规则性的空间相关性进行建模。从 3.6 公里长的直线轨道段的不规则性估计了几个具有不同复杂程度的多元模型。模型之间的比较表明,它们的选择不仅应基于统计信息标准,还应基于它们再现相关物理特征的能力。最后,一个案例研究表明,使用所提出的多变量过程合成的车辆对不规则性的响应比使用独立单变量过程合成的不规则性更好地匹配对测量数据的响应。从 3.6 公里长的直线轨道段的不规则性估计了几个具有不同复杂程度的多元模型。模型之间的比较表明,它们的选择不仅应基于统计信息标准,还应基于它们再现相关物理特征的能力。最后,案例研究表明,与使用独立单变量过程合成的不规则性相比,使用所提出的多变量过程合成的车辆对不规则性的响应与对测量数据的响应更好地匹配。从 3.6 公里长的直线轨道段的不规则性估计了几个具有不同复杂程度的多元模型。模型之间的比较表明,它们的选择不仅应基于统计信息标准,还应基于它们再现相关物理特征的能力。最后,案例研究表明,与使用独立单变量过程合成的不规则性相比,使用所提出的多变量过程合成的车辆对不规则性的响应与对测量数据的响应更好地匹配。还有他们复制相关物理特征的能力。最后,案例研究表明,与使用独立单变量过程合成的不规则性相比,使用所提出的多变量过程合成的车辆对不规则性的响应与对测量数据的响应更好地匹配。还有他们复制相关物理特征的能力。最后,案例研究表明,与使用独立单变量过程合成的不规则性相比,使用所提出的多变量过程合成的车辆对不规则性的响应与对测量数据的响应更好地匹配。

更新日期:2021-04-08
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