Fault diagnosis method of railway vehicle with wheel flat based on self-adaptive multi-scale morphology analysis
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摘要: 建立了56自由度车辆动力学模型与车轮扁疤模型, 计算了车辆的动态响应。车辆的振动信息往往受到轨道不平顺和车速波动等因素的影响, 为了能在强噪声背景下有效提取轮轨冲击特征, 提出了自适应多尺度形态学滤波分析方法, 研究了车轮扁疤引起的轴箱振动特征, 分析了轨道激扰和车辆运行速度对车轮扁疤故障诊断效果的影响。仿真结果表明: 在100、150、200km·h-1的车速和美国五级谱、三级谱的激扰下, 分别使用7个和9个尺度的结构元素进行形态学滤波, 正确地识别出10、15、20Hz车轮扁疤故障频率。实测结果表明: 当车速为40km·h-1时, 使用7个尺度的结构元素进行形态学滤波, 提取出了2 Hz的故障频率, 此频率与理论故障频率相对应, 诊断结果可靠。Abstract: A vehicle system dynamics model with 56 degrees of freedom and a wheel flat model were set up to calculate railway vehicle dynamic responses.The vibration information of vehicle was often influenced by various interferences, such as track irregularity and vehicle speed alteration.In order to effectively extract the wheel-track impact features from strong background noises, a self-adaptive multi-scale morphology filtering analysis algorithm was proposed to study the axle box vibration characteristics caused by wheel flat.The influences of track irregularity and vehicle running speed on the fault diagnosis result of axle box were discussed.Simulation result shows that the fault frequencies of 10, 15, 20 Hz are obtained by using morphology filter based on 7-scale and 9-scale structural elements at the speeds of 100, 150, 200km·h-1 with the American fifth grade and third grade track irregularities.Test result demonstrates that the fault frequency of 2 Hz is obtained by using morphology filter based on 7-scale structural element atthe speed of 40km·h-1, which is corresponding to the theoretic frequency of wheel flat, so diagnosis result is reliable.
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Key words:
- vehicle engineering /
- wheel flat /
- fault diagnosis /
- multi-scale morphology filter
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图 1 扁疤车辆轴箱振动信号与频谱
Figure 1. Vibration signal and frequency spectrum ofaxle box for vehicle with wheel flat
图 3 不平顺激励下的轴箱振动信号与频谱
Figure 3. Axle box vibration signal and frequency spectrumunder irregularity excitation
图 5 车速为150km·h-1时的轴箱振动信号与频谱
Figure 5. Axle box vibration signal and frequencyspectrum at 150km·h-1
图 7 车速200km·h-1时的轴箱振动信号与频谱
Figure 7. Axle box vibration signal and frequencyspectrum at 200km·h-1
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[1] 田丽丽, 方宗德, 赵勇. 铁路货车车轮踏面损伤检测中剥离与擦伤定位方法[J]. 铁道学报, 2009, 31(5): 31-36. doi: 10.3969/j.issn.1001-8360.2009.05.005TIAN Li-li, FANG Zong-de, ZHAO Yong. Location methods of peeling and flat spots in detection of wheel tread damages of railway wagons[J]. Journal of the China Railway Society, 2009, 31(5): 31-36. (in Chinese) doi: 10.3969/j.issn.1001-8360.2009.05.005 [2] BRIZUELA J, FRITSCH C, IBáÑEZ A. Railway wheel-flat detection and measurement by ultrasound[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(6): 975-984. doi: 10.1016/j.trc.2011.04.004 [3] 冯其波, 赵雁, 崔建英. 车轮踏面擦伤动态定量测量新方法[J]. 机械工程学报, 2002, 38(2): 120-122. doi: 10.3321/j.issn:0577-6686.2002.02.028FENG Qi-bo, ZHAO Yan, CUI Jian-ying. New dynamic method to quantitatively measure wheel flats of trains[J]. Chinese Journal of Mechanical Engineering, 2002, 38(2): 120-122. doi: 10.3321/j.issn:0577-6686.2002.02.028 [4] MILKOVIC' D, SIMIC' G, JAKOVLJEVIC' n. Wayside system for wheel-rail contact forces measurements[J]. Measurement, 2013, 46(9): 3308-3318. doi: 10.1016/j.measurement.2013.06.017 [5] FILOGRANO M L, GUILLéN P C, RODRíGUEZ-BARRIOS A, et al. Real-time monitoring of railway traffic using fiber bragg grating sensors[J]. IEEE Sensors Journal, 2012, 12(1): 85-92. doi: 10.1109/JSEN.2011.2135848 [6] BELOTTI V, CRENNA F, MICHELINI R C, et al. Wheel-flat diagnostic tool via wavelet transform[J]. Mechanical Systems and Signal Processing, 2006, 20(8): 1953-1966. doi: 10.1016/j.ymssp.2005.12.012 [7] 葛林富, 尹治本, 朱怀芳. 机车踏面擦伤计算机智能检测原理及实现[J]. 铁道学报, 1993, 15(2): 35-39. doi: 10.3321/j.issn:1001-8360.1993.02.005GE Lin-fu, YIN Zhi-ben, ZHU Huai-fang. Micro-computer intelligence detection of locomotive wheel tread irregularities[J]. Journal of the China Railway Society, 1993, 15(2): 35-39. doi: 10.3321/j.issn:1001-8360.1993.02.005 [8] GULLERS P, DREIK P, NIELSEN J C O, et al. Track condition analyser: identification of rail rolling surface defects, likely to generate fatigue damage in wheels, using instrumented wheelset measurements[J]. Journal of Rail and Rapid Transit, 2011, 225(1): 1-13. doi: 10.1243/09544097JRRT398 [9] MOLODOVA M, LI Z L, DOLLEVOET R. Axle box acceleration: measurement and simulation for detection of short track defects[J]. Wear, 2011, 271(1/2): 349-356. [10] DING Jian-ming, LIN Jian-hui, WANG Guang-ming, 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 [11] TSAI H C, WANG C Y, HUANG N E, et al. Railway track inspection based on the vibration response to a scheduled train and the Hilbert-Huang transform[J]. Journal of Rail and Rapid Transit, 2014, DOI: 10.1177/0954409714527930. [12] 胡爱军, 唐贵基, 安连锁. 基于数学形态学的旋转机械振动信号降噪方法[J]. 机械工程学报, 2006, 42(4): 127-130. doi: 10.3321/j.issn:0577-6686.2006.04.023HU Ai-jun, TANG Gui-ji, AN Lian-suo. De-noising technique for vibration signals of rotating machinery based on mathematical morphology filter[J]. Chinese Journal of Mechanical Engineering, 2006, 42(4): 127-130. (in Chinese) doi: 10.3321/j.issn:0577-6686.2006.04.023 [13] SERRA J, VINCENT L. An overview of morphological filtering[J]. Circuits, Systems and Signal Processing, 1992, 11(1): 47-108. doi: 10.1007/BF01189221 [14] 章立军, 徐金梧, 阳建宏, 等. 自适应多尺度形态学分析及其在轴承故障诊断中的应用[J]. 北京科技大学学报, 2008, 30(4): 441-445. doi: 10.3321/j.issn:1001-053X.2008.04.022ZHANG Li-jun, XU Jin-wu, YANG Jian-hong, et al. Adaptive multiscale morphology analysis and its application in fault diagnosis of bearings[J]. Journal of University of Science and Technology Beijing, 2008, 30(4): 441-445. doi: 10.3321/j.issn:1001-053X.2008.04.022 [15] LI Bing, ZHANG Pei-lin, WANG Zheng-jun, et al. Gear fault detection using multi-scale morphological filters[J]. Measurement, 2011, 44(10): 2078-2089. doi: 10.1016/j.measurement.2011.08.010 [16] ZHANG Li-jun, XU Jin-wu, YANG Jian-hong, et al. Multiscale morphology analysis and its application to fault diagnosis[J]. Mechanical Systems and Signal Processing, 2008, 22(3): 597-610. doi: 10.1016/j.ymssp.2007.09.010 [17] SAMANTA B, AL-BALUSHI K R, AL-ARAIMI S A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection[J]. Engineering Applications of Artificial Intelligence, 2003, 16(7/8): 657-665. [18] 李忠继, 魏来, 戴焕云, 等. 基于Hilbert-Huang变换的车轮扁疤识别方法[J]. 交通运输工程学报, 2012, 12(4): 33-41. doi: 10.3969/j.issn.1671-1637.2012.04.005LI Zhong-ji, WEI Lai, DAI Huan-yun, et al. Identification method of wheel flat based on Hilbert-Huang transform[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 33-41. (in Chinese) doi: 10.3969/j.issn.1671-1637.2012.04.005 [19] MAZILU T. A dynamic model for the impact between the wheel flat and rail[J]. UPB Scientific Bulletin Series D: Mechanical Engineering, 2007, 69(2): 45-58. -
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