基于Hilbert-Huang变换的车轮扁疤识别方法

LI Zhong-ji; WEI Lai; DAI Huan-yun; CENG Jing; WANG Yi-jia

doi:10.19818/j.cnki.1671-1637.2012.04.005

基于Hilbert-Huang变换的车轮扁疤识别方法

doi: 10.19818/j.cnki.1671-1637.2012.04.005

西南交通大学牵引动力国家重点实验室, 四川成都 610031

基金项目:

National Key Basic Research and Development Plan of China (973 Program) 20011CB711106

National Key Technology R & D Program in the 11th Five-year Plan of China 2009BAG12A02-E01-1

详细信息

中图分类号: U270.7
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出版历程
- 收稿日期: 2012-02-20
- 刊出日期: 2012-08-25

Identification method of wheel flat based on Hilbert-Huang transform

State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

Funds:

National Key Basic Research and Development Plan of China (973 Program) 20011CB711106

National Key Technology R & D Program in the 11th Five-year Plan of China 2009BAG12A02-E01-1

More Information

Author Bio:
LI Zhong-ji(1983-), Male, Chicheng, Hebei, Doctoral Student of Southwest Jiaotong University, Research on Vehicle Dynamics and Control, +86-28-86466221, tpllzj@gmail.com

DAI Huan-yun(1966-), Male, Chicheng, Hebei, Professor of Southwest Jiaotong University, PhD, +86-28-86466616, daihuanyun@163.com

Article Text (Baidu Translation)

摘要

摘要: 建立了56自由度车辆动力学模型和车轮扁疤模型, 计算了车辆的动态响应, 采用HHT时频分析方法研究了扁疤冲击引起的轴箱振动的HHT谱特征, 对比了车轮扁疤和车轮不圆的HHT谱特征之间的差异, 并分析了车辆运行速度和轨道激励对扁疤冲击振动的HHT谱的影响。分析结果表明: 健康车轮轴箱加速度的HHT谱呈现均匀分布, 带有扁疤车轮的轴箱加速度的HHT谱呈现纵向条带分布, 条带间隔与车速成反比, 车轮不圆的轴箱加速度呈现横向条带分布, 故根据轴箱加速度的HHT谱特征可以识别扁疤车轮。在京津线、国内既有线和美国三级谱上, 最低可辨识频率分别为30、50、80 Hz。车辆运行速度在150 km·h^-1以下时辨识频段取为40~100 Hz, 车辆运行速度在150 km·h^-1以上时辨识频段应加宽至200 Hz。
- 轨道车辆 /
- 车轮扁疤 /
- Hilbert-Huang变换 /
- 轴箱振动 /
- 车轮扁疤识别
Abstract: Vehicle system dynamics model with 56 degrees of freedom and wheel flat model were set up to compute vehicle dynamic responses.HHT(Hilbert-Huang transform) spectrum characteristics of axle box vibration caused by wheel flat impact were studied by using HHT time-frequency analysis method.HHT spectrum characteristics of axle box vertical accelerations caused by wheel flat and out-of-round were compared.The influences of vehicle running speed and track excitation on HHT spectrum characteristics of axle box vibration caused by wheel flat impact were studied.Analysis result shows that HHT spectrum distribution of axle box acceleration is uniform for healthy wheel, the stripe interval of vertical bar distribution for flat wheel is inversely proportional with the speed, and transverse bar distribution for out-of-round wheel.Therefore, wheel flat can be identified according to HHT spectrum characteristics of axle box acceleration.The lowest distinguishable frequencies are 30, 50, 80 Hz for Beijing-Tianjian Line irregularity, existing line irregularity in China and American three-grade track irregularity respectively.The identification band is 40-100 Hz when the running speed is lower than 150 km·h^-1, but it should be widened to 200 Hz when the speed is over 150 km·h^-1.
- rail vehicle /
- wheel flat /
- Hilbert-Huang transform /
- axle box vibration /
- identification of wheel flat

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Figure 1. Time histories of axle box vertical accelerations

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Figure 2. PSD values of axel box vertical accelerations

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Figure 3. IMF components of axle box vertical acceleration

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Figure 4. Residue of axle box vertical acceleration

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Figure 5. Error caused by EMD

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Figure 6. HHT spectrum of axle box vertical acceleration

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Figure 7. Time history and HHT spectrum of axle box vibration acceleration in case 1

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Figure 8. Time history and HHT spectrum of axle box vibration acceleration in case 2

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Figure 9. Time history and HHT spectrum of axle box vibration acceleration in case 3

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Figure 10. HHT spectrums of axle box vibration accelerations caused by out-of-round wheel

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Figure 11. HHT spectrums of axle box accelerations under different rail excitations

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Figure 12. HHT spectrums of axle box accelerations at different running speeds

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Figure 13. HHT spectrum of test 1

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Figure 14. HHT spectrum of test 2

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Table 1. Statistical indices of axle box vertical accelerations

Acceleration RMS/g 95% value/g Maximum/g

Simulation 0.965 0.662 3.891

Test 1.064 0.607 3.993

Error/% 9.30 8.38 2.51

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