Wheel polygon state recognition method based on improved EEMD-WVD joint time-frequency analysis
-
摘要: 为了准确识别高速列车车轮多边形状态以及磨耗幅值，提出了一种改进的聚合经验模态分解(EEMD)与魏格纳-威尔分布(WVD)相结合的随机振动信号联合时频分析方法；利用相关系数法和频谱分析来评估筛选轴箱振动加速度信号经EEMD分解后的变量，然后进行WVD计算，在保持WVD高时频分辨率的同时可有效抑制交叉干扰项；应用该方法分析了周期性车轮多边形磨耗与现场实测随机车轮多边形磨耗引起的轴箱振动加速度信号。研究结果表明：利用EEMD-WVD二维时频谱的主频率可识别车轮多边形状态，利用EEMD-WVD三维时频能量谱的能量幅值分布可评估车轮多边形磨耗幅值，最大误差为0.3%；将改进EEMD和WVD联合时频分析方法的识别结果与短时傅里叶变换、小波分解、WVD传统时频分析方法进行对比，表明此方法应用时无需改变任何参数，自适应强，保留了WVD高时频分辨率的特点，而且可有效抑制EEMD产生的模态混叠现象和WVD产生的交叉干扰项，验证了所提出联合时频分析方法的有效性及其优势，为高速动车组车轮多边形识别和评估提供了新的技术途径。Abstract: To accurately identify wheel polygon state and wheel abrasion amplitude of high-speed EMUs, a random vibration signal's joint time-frequency analysis method combing improved ensemble empirical mode decomposition (EEMD) and Wigner-Ville distribution (WVD) was presented. The correlation coefficient method and spectrum analysis were used to evaluate and filter the EEMD decomposition variables of the axle box vibration acceleration signal. Subsequently, the WVD of each intrinsic mode component was calculated for maintaining a high time-frequency resolution and effectively curbing cross-interference items. The method was applied to analyze the vibration acceleration signals of axle box caused by periodic and measured random wheel polygons. Research results indicate that the wheel polygon type can be recognized using the dominant frequency of the two-dimensional time-frequency spectrum of EEMD-WVD, and the abrasion amplitude can be evaluated using the energy amplitude distribution of the three-dimensional time-frequency-energy spectrum of EEMD-WVD, with the maximum error of 0.3%. Compared with the time-frequency analytical results by the short-time Fourier transform, wavelet transforms, and WVD method, the improved EEMD-WVD joint time-frequency analysis method dose not require parametric variation, has strong adaptability, retains the characteristics of WVD high time-frequency resolution, and effectively curbs both the modal aliasing phenomenon caused by EEMD decomposition and the cross-interference items caused by WVD distribution. The current study verifies the effectiveness and advantages of the proposed joint time-frequency analysis method, providing a novel technical approach for wheel polygon recognition and the evaluation of high-speed EMUs. 1 tab, 11 figs, 30 refs.
-
图 1 改进EEMD-WVD联合时频分析流程
Figure 1. Flow of joint time-frequency analysis based on improved EEMD-WVD
图 2 车轮多边形作用下轴箱振动加速度信号峰值特征
Figure 2. Characteristics of peak values of axle box acceleration signal caused by wheel polygons
图 4 轴箱振动加速度信号频谱降噪前后对比
Figure 4. Comparison of vibration acceleration signal frequency spectra of axle box before and after noise reduction
图 6 轴箱加速度信号EEMD-WVD时频谱
Figure 6. Time-frequency spectrum of axle box acceleration signal based on EEMD-WVD
图 7 轴箱振动加速度信号EEMD-WVD时频能量谱
Figure 7. Time-frequency-energy spectra based on EEMD-WVD of vibration acceleration signals of axle box
图 10 轴箱振动加速度信号及其EEMD-WVD联合时频变换谱
Figure 10. Vibration acceleration signal of axle box and its joint time-frequency transform spectra
图 11 不同方法的时频分析结果对比
Figure 11. Comparison of time-frequency analysis results by different methods
表 1 分量相关系数
Table 1. Correlation coefficients of components
IMF IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7 IMF8 系数 0.972 0.061 0.066 0.138 0.091 0.062 0.012 0.003 
下载: 导出CSV
-
[1] 查浩, 任尊松, 徐宁. 车轮扁疤激起的轴箱轴承冲击特性[J]. 交通运输工程学报, 2020, 20(4): 165-173. doi: 10.19818/j.cnki.1671-1637.2020.04.013ZHA Hao, REN Zun-song, XU Ning. Impact characteristics of axle box bearing due to wheel flat scars[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 165-173. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.04.013 [2] 刘佳, 韩健, 肖新标, 等. 高速车轮非圆化磨耗对轴箱端盖异常振动影响初探[J]. 机械工程学报, 2017, 53(20): 98-105. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201720013.htmLIU Jia, HAN Jian, XIAO Xin-biao, et al. Influence of wheel non-circular wear on axle box cover abnormal vibration in high-speed train[J]. Journal of Mechanical Engineering, 2017, 53(20): 98-105. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201720013.htm [3] 向俊, 袁铖, 余翠英, 等. 高速铁路无砟轨道扣件弹条断裂原因分析[J]. 铁道科学与工程学报, 2019, 16(7): 1605-1613. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201907001.htmXIANG Jun, YUAN Cheng, YU Cui-ying, et al. Analysis of elastic bar fracture causes of fasteners in ballastless track of high-speed railway[J]. Journal of Railway Science and Engineering, 2019, 16(7): 1605-1613. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201907001.htm [4] 邹航宇, 张卫华, 王志伟. 车轮多边形化对高速列车齿轮箱体动态响应的影响[J]. 机车电传动, 2017(6): 52-56. https://www.cnki.com.cn/Article/CJFDTOTAL-JCDC201706014.htmZOU Hang-yu, ZHANG Wei-hua, WANG Zhi-wei. Influence of wheel polygonization on dynamic response of gearbox housing of high-speed train[J]. Electric Drive for Locomotives, 2017(6): 52-56. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JCDC201706014.htm [5] 王文静, 李广全, 韩俊臣, 等. 高速列车齿轮箱箱体动应力影响规律[J]. 交通运输工程学报, 2019, 19(1): 85-95. doi: 10.3969/j.issn.1671-1637.2019.01.009WANG Wen-jing, LI Guang-quan, HAN Jun-chen, et al. Influence rule of dynamic stress of high-speed train gearbox housing[J]. Journal of Traffic and Transportation Engineering, 2019, 19(1): 85-95. (in Chinese) doi: 10.3969/j.issn.1671-1637.2019.01.009 [6] 赵玲, 黄大荣, 王宏刚. 改进Hilbert-Huang变换在轨道不平顺中的应用[J]. 控制工程, 2019, 26(8): 1539-1543. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF201908018.htmZHAO Ling, HUANG Da-rong, WANG Hong-gang. Analysis of track irregularity based on the improved Hilbert-Huang transform[J]. Control Engineering of China, 2019, 26(8): 1539-1543. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF201908018.htm [7] 陶伟文, 刘韦, 罗跟东, 等. 基于小波分析的铁路车辆轮对纵向非稳态振动研究[J]. 噪声与振动控制, 2019, 39(4): 125-129. doi: 10.3969/j.issn.1006-1355.2019.04.023TAO Wei-wen, LIU Wei, LUO Gen-dong, et al. Study on railway wheelset longitudinal non-stationary vibration based on wavelet analysis[J]. Noise and Vibration Control, 2019, 39(4): 125-129. (in Chinese) doi: 10.3969/j.issn.1006-1355.2019.04.023 [8] 曹西宁, 柴晓冬, 郑树彬. 基于Hilbert-Huang变换的轨道车辆轴箱加速度信号分析[J]. 仪表技术与传感器, 2015(3): 92-95. doi: 10.3969/j.issn.1002-1841.2015.03.029CAO Xi-ning, CHAI Xiao-dong, ZHENG Shu-bin. Analysis of acceleration of train axle box based on Hilbert-Huang transformation[J]. Instrument Technique and Sensor, 2015(3): 92-95. (in Chinese) doi: 10.3969/j.issn.1002-1841.2015.03.029 [9] SALVADOR P, NARANJO V, INSA R, et al. Axlebox accelerations: their acquisition and time-frequency characterisation for railway track monitoring purposes[J]. Measurement, 2016, 82: 301-312. doi: 10.1016/j.measurement.2016.01.012 [10] 张博, 刘秀波, 马帅, 等. 基于时频分析的高速铁路路基局部变形识别方法[J]. 铁道建筑, 2019, 59(12): 95-98. https://www.cnki.com.cn/Article/CJFDTOTAL-TDJZ201912022.htmZHANG Bo, LIU Xiu-bo, MA Shuai, et al. Local deformation identification method for high speed railway subgrade based on time-frequency analysis[J]. Railway Engineering, 2019, 59(12): 95-98. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDJZ201912022.htm [11] CAO H R, FAN F, ZHOU K, et al. Wheel-bearing fault diagnosis of trains using empirical wavelet transform[J]. Measurement, 2016, 82: 439-449. doi: 10.1016/j.measurement.2016.01.023 [12] DING J M, LIN J H, WANG G M, et al. Time-frequency analysis of wheel-rail shock in the presence of wheel flat[J]. Journal of Traffic and Transportation Engineering (English Edition), 2014, 1(6): 457-466. doi: 10.1016/S2095-7564(15)30296-8 [13] 韩宗炎, 曹扬, 董璐, 等. 基于改进的WVD的城轨列车车轮扁疤分析研究[J]. 现代制造技术与装备, 2017(9): 5-8, 10. doi: 10.3969/j.issn.1673-5587.2017.09.003HAN Zong-yan, CAO Yang, DONG Lu, et al. Experimental study on wheel flat of urban rail transit based on improved WVD[J]. Modern Manufacturing Technology and Equipment, 2017(9): 5-8, 10. (in Chinese) doi: 10.3969/j.issn.1673-5587.2017.09.003 [14] LIANG B, IWNICKI S, BALL A, et al. Adaptive noise cancelling and time-frequency techniques for rail surface defect detection[J]. Mechanical Systems and Signal Processing, 2015, 54/55: 41-51. doi: 10.1016/j.ymssp.2014.06.012 [15] PACHORI R B, NISHAD A. Cross-terms reduction in the Wigner-Ville distribution using tunable-Q wavelet transform[J]. Signal Processing, 2016, 120: 288-304. doi: 10.1016/j.sigpro.2015.07.026 [16] 李奕璠, 刘建新, 李忠继. 基于Hilbert-Huang变换的列车车轮失圆故障诊断[J]. 振动、测试与诊断, 2016, 36(4): 734-739, 812-813. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201604021.htmLI Yi-fan, LIU Jian-xin, LI Zhong-ji. The fault diagnosis method of railway out-of-round wheels using Hilbert-Huang transform[J]. Journal of Vibration, Measurement and Diagnosis, 2016, 36(4): 734-739, 812-813. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201604021.htm [17] 谷然, 陈捷, 洪荣晶, 等. 基于改进自适应变分模态分解的滚动轴承微弱故障诊断[J]. 振动与冲击, 2020, 39(8): 1-7, 22. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202008001.htmGU Ran, CHEN Jie, HONG Rong-jing, et al. Early fault diagnosis of rolling bearings based on adaptive variational mode decomposition and the Teager energy operator[J]. Journal of Vibration and Shock, 2020, 39(8): 1-7, 22. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202008001.htm [18] 秦娜, 王开云, 金炜东, 等. 高速列车转向架故障的经验模态熵特征分析[J]. 交通运输工程学报, 2014, 14(1): 57-64, 74. doi: 10.3969/j.issn.1671-1637.2014.01.010QIN Na, WANG Kai-yun, JIN Wei-dong, et al. Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 57-64, 74. (in Chinese) doi: 10.3969/j.issn.1671-1637.2014.01.010 [19] 蔡艳平, 李艾华, 王涛, 等. 基于EMD-Wigner-Ville的内燃机振动时频分析[J]. 振动工程学报, 2010, 23(4): 430-437. doi: 10.3969/j.issn.1004-4523.2010.04.012CAI Yan-ping, LI Ai-hua, WANG Tao, et al. I.C. engine vibration time-frequency analysis based on EMD-Wigner-Ville[J]. Journal of Vibration Engineering, 2010, 23(4): 430-437. (in Chinese) doi: 10.3969/j.issn.1004-4523.2010.04.012 [20] PENG Z K, TSE P W, CHU F L. An improved Hilbert-Huang transform and its application in vibration signal analysis[J]. Journal of Sound and Vibration, 2005, 286(1/2): 187-205 [21] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-51. doi: 10.1142/S1793536909000047 [22] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995. doi: 10.1098/rspa.1998.0193 [23] 窦希杰, 王世博, 谢洋, 等. 基于IMF能量矩和SVM的煤矸识别[J]. 振动与冲击, 2020, 39(24): 39-45. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202024007.htmDOU Xi-jie, WANG Shi-bo, XIE Yang, et al. Coal and gangue identification based on IMF energy moment and SVM[J]. Journal of Vibration and Shock, 2020, 39(24): 39-45. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202024007.htm [24] STEVENSON N, MESBAH M, BOASHASH B. A sampling limit for the empirical mode decomposition[C]//IEEE. 8th International Symposium on Signal Processing and its Applications. New York: IEEE, 2005: 647-650. [25] SONG Y, DU Y L, ZHANG X M, et al. Evaluating the effect of wheel polygons on dynamic track performance in high-speed railway systems using co-simulation analysis[J]. Applied Sciences, 2019, 9(19): 4165. doi: 10.3390/app9194165 [26] 陈美, 翟婉明, 閤鑫, 等. 高速铁路多边形车轮通过钢轨焊接区的轮轨动力特性分析[J]. 科学通报, 2019, 64(25): 2573-2582. https://www.cnki.com.cn/Article/CJFDTOTAL-KXTB201925004.htmCHEN Mei, ZHAI Wan-ming, GE Xin, et al. Analysis of wheel-rail dynamic characteristics due to polygonal wheel passing through rail weld zone in high-speed railways[J]. Chinese Science Bulletin, 2019, 64(25): 2573-2582. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KXTB201925004.htm [27] 姚建伟, 胡晓依, 成棣, 等. 高速轮轨磨耗机理及减磨控制技术措施研究[R]. 北京: 中国铁道科学研究院, 2016.YAO Jian-wei, HU Xiao-yi, CHENG Di, et al. Research on wear mechanism and control technology of high speed wheel-rail reduction[R]. Beijing: China Academy of Railway Sciences, 2016. (in Chinese) [28] WU Yue, DU Xing, ZHANG He-ji, et al. Experimental analysis of the mechanism of high-order polygonal wear of wheels of a high-speed train[J]. Journal of Zhejiang University—Science A (Applied Physics and Engineering), 2017, 18(8): 579-592. doi: 10.1631/jzus.A1600741 [29] 朱海燕, 胡华涛, 尹必超, 等. 轨道车辆车轮多边形研究进展[J]. 交通运输工程学报, 2020, 20(1): 102-119. doi: 10.19818/j.cnki.1671-1637.2020.01.008ZHU Hai-yan, HU Hua-tao, YIN Bi-chao, et al. Research progress on wheel polygons of rail vehicles[J]. Journal of Traffic and Transportation Engineering, 2020, 20(1): 102-119. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.01.008 [30] 杨润芝, 曾京. 高阶车轮多边形对轮轨系统振动影响分析[J]. 振动与冲击, 2020, 39(21): 101-110. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202021015.htmYANG Run-zhi, ZENG Jing. Influences of higher order wheel polygon on vibration of wheel-rail system[J]. Journal of Vibration and Shock, 2020, 39(21): 101-110. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202021015.htm -
点击查看大图
图(11) / 表(1)
计量
- 文章访问数: 376
- HTML全文浏览量: 158
- PDF下载量: 45
- 被引次数: 0
