Identification of Railway Pantograph-Catenary Contact Force Based on Data Modeling

被引：1
|
作者：
Guo J. [1 ]
Ke Z. [1 ]
Liu J. [1 ]
Zhang W. [1 ]
Yang Z. [1 ]
Cui W. [2 ]
机构：
[1] Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing
[2] School of Electrical Engineering, Beijing Jiaotong University, Beijing
来源：
Zhongguo Tiedao Kexue/China Railway Science | 2021年 / 42卷 / 04期
关键词：
Data modeling; Hard spot; Load identification; Neural network; Pantograph; Pantograph-catenary contact force; Vibration acceleration;
D O I：
10.3969/j.issn.1001-4632.2021.04.17
中图分类号：
学科分类号：
摘要：
On the basis of analyzing the application condition of load identification technology and the comprehensive inspection data of the railway pantograph-catenary, firstly, time domain analysis, frequency domain analysis, trend item elimination and segment fusion and other pretreatments are carried out combined with data characteristics. Then, three data modeling methods of BP, ELM and LSTM neural network in machine learning are used. Taking the 4 pantograph vibration accelerations and 2 hard spot vibration accelerations as inputs respectively, the pantograph-catenary contact force is identified by data modeling and compared with field test results. The results show that the numerical models established by the three kinds of neural networks can identify the large value of pantograph-catenary contact force through the data of pantograph and hard spot vibration accelerations. The numerical model established with 4 vibration accelerations has better identification effect than that with 2 hard spot accelerations. The numerical model established with LSTM neural network avoids the problems of gradient explosion and gradient disappearance during the training process, and has higher identification correlation. © 2021, Editorial Department of China Railway Science. All right reserved.
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页码：145 / 154
页数：9
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