Original scientific paper

https://doi.org/10.17559/TV-20210824045232

Wheel Weighing Meter of Continuous Rail Based on BP Neural Network and Symmetric Moving Average Filter

Hao Xue ; China Academy of Railway Sciences Corporation Limited, 2 Daliushu Road, Haidian District, Beijing, China
Yiran Liu* ; China Academy of Railway Sciences Corporation Limited, 2 Daliushu Road, Haidian District, Beijing, China
Chao Li ; China Academy of Railway Sciences Corporation Limited, 2 Daliushu Road, Haidian District, Beijing, China
Feng Gao ; China Academy of Railway Sciences Corporation Limited, 2 Daliushu Road, Haidian District, Beijing, China

Abstract

Regularly detecting the wheel weight of EMU (Electric Multiple Units) is of great significance for maintaining the safety of railway transportation. The existing wheel weighing instruments are mostly the rail-broken type, which is easy to cause safety risks in long-term use. A rail-continues wheel weighing meter is introduced to solve the problem. The rail-continues wheel weight meter introduces a composite sensor structure, using BP neural network to search the optimal sensor factors and introduces a parameter to eliminate the interference of the EMU driving speed. To process the output of the BP neural network, a symmetric moving average filtering algorithm is proposed. The experimental results show that the wheel weighing meter introduced in this paper has high precision and stability. The weighing error of the continuous rail wheel weighing meter is 0.24%.

Keywords

BP neural network; composite sensors; railway transport; signal filter; wheel weighing

Hrčak ID:

269552

URI

https://hrcak.srce.hr/269552

Publication date:

15.2.2022.

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