Fault feature analysis of high-speed train bogie based on empirical mode decomposition entropy
-
摘要: 针对故障发生时高速列车转向架振动信号的特点, 提出了基于聚合经验模态分解和5种信息熵相结合的特征提取方法。首先将振动信号进行聚合经验模态分解, 有效地避免了模态混叠问题, 然后对分解得到的本征模态函数提取反映信号复杂度的经验模态熵特征。利用该方法对高速列车转向架正常与空气弹簧、横向减振器、抗蛇行减振器故障4种工况下280个样本数据进行特征分析, 随机取60%为训练样本, 其余40%为测试样本。分析结果表明: 分解过程不需要选择基函数和分解层数, 因此, 此方法具有良好的自适应性。在运行速度为200km·h-1时, 识别率大于95%, 证明了该特征提取方法对于高速列车转向架故障振动信号分析的有效性。Abstract: A novel method of feature extraction was proposed by combining ensemble empirical mode decomposition (EEMD) and five entropies based on the characteristics of vibration signal for high-speed train bogie in failure station. Firstly, vibration signal was decomposed by EEMD to avoid mode mixing effectively. Secondly, EEMD entropy feature was calculated for describing the complexity of intrinsic mode functions (IMFs). Vibration signals were obtained under four typical working conditions including normal condition, air spring fault, lateral damper fault and yaw damper fault. There were 280 sample data including 60% training samples and 40% test samples. Analysis result shows that the method is good adaptivity for unselecting basis functions and decomposition levels. The recognition rate is above 95% at the running speed of 200 km·h-1. Therefore, the feature extraction method is effective to analyze the vibration signal of high-speed train bogie in fault station.
-
图 2 四种工况下的时域信号与幅值谱
Figure 2. Time domain signals and amplitude spectrums under four working conditions
表 1 不同工况下经验模态熵
Table 1. Empirical mode decomposition entropies under different working conditions
下载: 导出CSV
表 3 不同特征提取方法的识别率对比
Table 3. Comparison of fault recognition rates for different feature extraction methods
下载: 导出CSV
-
[1] 丁建明, 林建辉, 赵洁, 等. 车辆悬挂弹簧故障检测的能量传递特性比较法[J]. 交通运输工程学报, 2013, 13 (4): 51-55, 62. doi: 10.3969/j.issn.1671-1637.2013.04.008DING Jian-ming, LIN Jian-hui, ZHAO Jie, et al. Comparison method of energy transfer characteristics for fault detection of vehicle suspension spring[J]. Journal of Traffic and Transportation Engineering, 2013, 13 (4): 51-55, 62. (in Chinese). doi: 10.3969/j.issn.1671-1637.2013.04.008 [2] 颜秋, 刘永明. 基于MATLAB/Simulink的车辆建模与故障分析[J]. 华东交通大学学报, 2012, 29 (5): 13-17. https://www.cnki.com.cn/Article/CJFDTOTAL-HDJT201205004.htmYAN Qiu, LIU Yong-ming. The analysis of vehicle model establishment and malfunction based on MATLAB/Simulink[J]. Journal of East China Jiaotong University, 2012, 29 (5): 13-17. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HDJT201205004.htm [3] 韩清鹏. 利用EEG信号的小波包变换与非线性分析实现精神疲劳状态的判定[J]. 振动与冲击, 2013, 32 (2): 182-188. doi: 10.3969/j.issn.1000-3835.2013.02.035HAN Qing-peng. Evaluation of human mental stress states based on wavelet package transformation and nonlinear analysis of EEG signals[J]. Journal of Vibration and Shock, 2013, 32 (2): 182-188. (in Chinese). doi: 10.3969/j.issn.1000-3835.2013.02.035 [4] 黄娟, 黄纯, 江亚群, 等. 基于小波包近似熵的线路故障性质辨识方法[J]. 仪器仪表学报, 2012, 33 (9): 2009-2015. doi: 10.3969/j.issn.0254-3087.2012.09.013HUANG Juan, HUANG Chun, JIANG Ya-qun, et al. Identification method of fault characteristics in transmission lines based on wavelet packet and approximate entropy[J]. Chinese Journal of Scientific Instrument, 2012, 33 (9): 2009-2015. (in Chinese). doi: 10.3969/j.issn.0254-3087.2012.09.013 [5] SESHADRINATH J, SINGH B, PARNIGRAHI B K. Vibration analysis based interturn fault diagnosis in induction machines[J]. IEEE Transactions on Industrial Informatics. 2014, 10 (1): 340-350. [6] WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[R]. Calverton: Center for Ocean-Land-Atmosphere Studies, 2009. [7] WU Z H, HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[C]∥The Royal Society. Proceedings of the Royal Society, Series A: Mathematical, Physical and Engineering Sciences. London: The Royal Society, 2004: 1597-1611. [8] 胡爱军, 马万里, 唐贵基. 基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J]. 中国电机工程学报, 2012, 32 (11): 106-111. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201211016.htmHU Ai-jun, MA Wan-li, TANG Gui-ji. Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and kurtosis criterion[J]. Proceedings of the CSEE, 2012, 32 (11): 106-111. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201211016.htm [9] LEI Ya-guo, HE Zheng-jia, ZI Yan-yang. EEMD method and WNN for fault diagnosis of locomotive roller bearings[J]. Expert Systems with Applications, 2011, 38 (6): 7334-7341. [10] ZVOKELJ M, ZUPAN S, PREBIL I. Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method[J]. Mechanical Systems and Signal Processing, 2011, 25 (7): 2631-2653. [11] 张学清, 梁军. 基于EEMD-近似熵和储备池的风电功率混沌时间序列预测模型[J]. 物理学报, 2013, 62 (5): 76-85. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201305009.htmZHANG Xue-qing, LIANG Jun. Chaotic time series prediction model of wind power based on ensemble empirical mode decomposition-approximate entropy and reservoir[J]. Acta Physica Sinica, 2013, 62 (5): 76-85. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201305009.htm [12] HUANG Jian, HU Xiao-guang, GENG Xin. An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine[J]. Electric Power Systems Research, 2011, 81 (2): 400-407. [13] LABATE D, FORESTA F L, MORABITO G, et al. Entropic measures of EEG complexity in alzheimer's disease through a multivariate multiscale approach[J]. IEEE Sensors Journal, 2013, 13 (9): 3284-3292. [14] HE Zheng-you, CHEN Xiao-qing, LUO Guo-ming. Wavelet entropy measure definition and its application for transmission line fault detection and identification, partⅠ: definition and methodology[C]∥IEEE. 2006International Conference on Power System Technology. Chongqing: IEEE, 2006: 1-6. [15] AN Xue-li, JIANG Dong-xiang, LI Shao-hua, et al. Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine[J]. Energy, 2011, 36 (9): 5508-5520. -
点击查看大图
图(11) / 表(3)
计量
- 文章访问数: 732
- HTML全文浏览量: 93
- PDF下载量: 2000
- 被引次数: 0
