基于改进EEMD样本熵的高速列车滚子缺陷AE信号提取

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (16) : 148-154.

论文

邓韬，林建辉，黄晨光，靳行，张敏

作者信息 +

AE signal extraction ofHigh SpeedTrainroller based on modified EEMD and Segment sample entropy

DENGTao, LINJianhui,HUANGChenguang,JINHang,ZHANG Min

Author information +

文章历史 +

摘要

提出一种改进EEMD滚子缺陷声发射（AE）信号提取新方法，该方法根据EMD分解的二进滤波器组特性构造幅值与频率成线性—正弦规律变化的噪声添加进测试数据，给出了噪声构建原则，并按频率由高到低限定各阶IMF筛选次数，选取同一时段高频IMF归一化样本熵占比较大的数据段作为声发射事件参考。对实测数据计算表明特殊构造的噪声和筛选次数能有效抑制中低频段模态混叠和高阶IMF小波消失现象，改进后的EEMD方法分解出的IMF分量物理意义明确，性能优于传统EEMD方法。分段的IMF样本熵能在连续监测中捕捉声发射事件，对应的Hilbert谱能直观凸显出滚子缺陷声发射信号，滚动体AE信号事件周期与理论计算相吻合。

Abstract

A new method of High Speed Train rollerAE signal extraction was presented here.BecauseEMD acts as a dyadic filter bank, in this method,amplitude of the added noise in accordance with a Linear - Sinusoidal spectrum, and expound how to assemble the noise. The sifting numberwas set by frequency from high to low.Then,calculate IMF’s Segment sample entropy along the timeline, which take a larger proportionwas identified as an AE events. The experimental result shows that the Linear - Sinusoidal noise spectrum and sifting number couldrestrain the mode mixing and the little wave vanish.The modified EEMDobtains a tangible physical meaning and improved results compared with the original EEMD.Segment sample entropy could captured the AE events in a continuous monitoring data. The AE signal was intuitive reflect in the correspondingHilbertspectrogram. TherollerAE signal are consistent with the theoretical calculation.

导出引用

邓韬，林建辉，黄晨光，靳行，张敏. 基于改进EEMD样本熵的高速列车滚子缺陷AE信号提取[J]. 振动与冲击, 2017, 36(16): 148-154

DENGTao, LINJianhui,HUANGChenguang,JINHang,ZHANG Min. AE signal extraction ofHigh SpeedTrainroller based on modified EEMD and Segment sample entropy[J]. Journal of Vibration and Shock, 2017, 36(16): 148-154

参考文献

[1] 刘方,沈长青等.基于时域多普勒校正和EEMD的列车轴承道旁声音监测故障诊断方法研究[J] . 振动与冲击,2013, 32 (24): 104-109.
LIU Fang, SHEN Chang-qing. Wayside acoustic fault diagnosis for locomotive bearings based onDoppler effect correction and EEMD method in time domain [J]. Journal of Vibration and Shock, 2013, 32 (24): 104-109.
[2] WANG K S, HEYNS P S. Application of computed order tracking,Vold Kalman filtering and EMD in rotating machine vibration [J]. Mechanical Systems and Signal Processing, 2011, 25(1): 416-430.　
[3] YUNLONG Z, PENG Z. Vibration fault diagnosis method of centrifugal pump based on END complexity feature and least square support vector machine [J]. Energy Procedia, 2012, 17: 939-945.
[4] WU Z H,HUANG N E. Ensemble empirical mode Decomposition:A noise assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1:1-41.
[5] 沈长青、谢伟达等.基于EEMD和改进的形态滤波方法的轴承故障诊断研究[J].振动与冲击,2013,32(2): 39-43.
SHEN Chang-qing, Peter W. Tse. Rolling element bearing fault diagnosis based on EEMDand improved morphological filtering method [J]. Journal of Vibration and Shock, 2013,32(2): 39-43.
[6] Lei Y,He Zi Y.Application of the EEMD method to rotorfault diagnosis of rotating machinery [J].Mechanical Systems and Signal Processing,2009,23(4):1327-1338.
[7] 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.
HU Aijun, MA Wanli. Rolling Bearing Fault Feature Extraction Method Based on Ensemble Empirical ModeDecomposition and Kurtosis Criterion[J].Proceedings of the CSEE, 2012,32(11): 106-111.
[8] 雷亚国、孔德同等.自适应总体平均经验模式分解及其在行星齿轮箱故障检测中的应用[J].机械工程学报. 2014，50（3）：64-70.
LEI Yaguo, KONG Detong.Adaptive Ensemble Empirical Mode Decomposition and Its Application to Fault Detection of Planetary Gearboxes[J]. Journal of Mechanical Engineering.
[9] FLANDRIN P，GABRIEL R，PAULO G. Empirical mode decomposition as a filter bank[J]. IEEE Signal Processing Letter，2004,11:112-114.
[10] Zhaohua Wu, Huang Norden E. A study of the characteristics of white noise using the empirical mode decomposition method[J].Proc.R.Soc.Lond,2004, 454: 1597-1611.
[11] Richman J S Moorman J R 2000Am [J].Physiol Heart Circ.Physiol.2782039.
[12] 陈双喜,林建辉,陈建政. 基于希尔伯特-黄变换提取车桥耦合系统时频特性[J].振动与击,2012, 31(15): 175-179.
CHEN Shuang-xi,LIN Jian-hui. Time-frequency characteristics extraction for vehicle/bridgecoupled system based on Hilbert-Huang transformation [J]. Journal of Vibration and Shock,2012, 31(15): 175-179.
[13] Pincus S M．Approximate entropy as a measure of system complexity[J]. Proc Natl Acad Sci USA, 1991, 88:2297—2301.