Time-frequency characterization of rail corrugation under a combined auto-regressive and matched filter scheme
Time-frequency characterization of rail corrugation under a combined auto-regressive and matched filter scheme
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DOI：
10.1016/j.ymssp.2011.12.015
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发表时间：
2012-05-01
影响因子：
8.4
通讯作者：
Aknin, P.
中科院分区：
文献类型：
--
作者：
Hory, C.;Bouillaut, L.;Aknin, P.
钢轨磨耗是钢轨与车轮长期相互作用引起的钢轨表面振动性机械磨损。例如，如欧洲标准所推荐的那样，在里程域或波长域中设计用于波长监测的信号处理方法。然而，监测数据的联合里程和波长域分析可以提供关于双模式的同时幅度和波长调制的关键信息。本文提出了使用自回归匹配滤波器组（AR-MAFI）方法进行这样的里程-波长域分析的轨道故障。我们表明，这些方法假设一个统计模型，适合的数据。我们还讨论了最佳的参数设置，用于分析的数据。从法国RATP地铁网络收集的数据进行的实验研究表明，AR-MAFI方法优于（在可读性和准确性方面）的标准距离域或波长域的方法在本地化和表征的干扰。(C)2011爱思唯尔有限公司版权所有。
Rail corrugation is an oscillatory mechanical wear of rail surface raising from the long-term interaction between rail and wheel. Signal processing approaches to corrugation monitoring, as recommended by the European standards for instance, are designed either in the mileage domain or in the wavelength domain. However a joint mileage and wavelength domain analysis of the monitoring data can provide crucial information about the simultaneous amplitude and wavelength modulations of the corrugation modes. It is proposed in this paper to perform such a mileage-wavelength domain analysis of rail corrugation using the class of Auto-Regressive-MAtched Filterbank (AR-MAFI) methods. We show that these methods assume a statistical model that fits the corrugation data. We discuss also the optimal parameter settings for the analysis of corrugation data. Experimental studies performed on data collected from the French RATP metro network show that the AR-MAFI methods outperform (in terms of readability and accuracy) the standard distance domain or wavelength domain methods in localizing and characterizing corrugation. (C) 2011 Elsevier Ltd. All rights reserved.