Mobile Device-Based Train Ride Comfort Measuring System

被引:4
|
作者
Hu, Yuwei [1 ]
Xu, Lanxin [1 ]
Wang, Shuangbu [2 ]
Gu, Zhen [1 ]
Tang, Zhao [1 ]
机构
[1] Southwest Jiaotong Univ, Tract Power Natl Key Lab, Chengdu 610031, Peoples R China
[2] Southwest Jiaotong Univ, Inst Smart City & Intelligent Transportat, Chengdu 611756, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 14期
关键词
rail vehicle; ride comfort; vibration measurement; noise measurement; Sperling index; VIBRATION;
D O I
10.3390/app12146904
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
As an important train performance quality, comfort depends on vibration and noise data measured on a running train. Traditional vibration and noise measurement tools are facing challenges in terms of collecting big data, portability, and cost. With the continuous upgrade of mobile terminal hardware, the built-in sensors of mobile phones have the ability to undertake relatively complex data measurement and processing tasks. In this study, a new type of train comfort measurement system based on a mobile device is developed by using a built-in sensor to measure the vibration and noise. The functions of the developed system include the real-time display of three-way vibration acceleration, lateral and vertical Sperling indicators, sound pressure level, and train comfort-related data storage and processing. To verify the accuracy of the mobile device-based train ride comfort measuring system (DTRCMS), a comparison of test results from this system and from the traditional measuring system is conducted. The comparison results show that the DTRCMS is in good agreement with the traditional measuring system. The relative error in three-direction acceleration and Sperling values is 2 similar to 10%. The fluctuation range of the noise measured by DTRCMS is slightly lower than that of the professional noise meter, and the relative error is mainly between 1.5% and 4.5%. Overall, the study shows that using mobile devices to measure train comfort is feasible and practical and has great potential for big data-based train comfort evaluation in the future.
引用
收藏
页数:13
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