基于EEMD-CWD的齿轮箱振动信号故障特征提取

doi:10.3969/j.issn.1000-1093.2014.08.023

兵工学报 ›› 2014, Vol. 35 ›› Issue (8): 1288-1294.doi: 10.3969/j.issn.1000-1093.2014.08.023

基于EEMD-CWD的齿轮箱振动信号故障特征提取

王卫国^1,2，孙磊³

(1.北京理工大学管理与经济学院，北京 100081； 2.军械工程学院科研部，河北石家庄 050003；

收稿日期:2013-11-19 修回日期:2013-11-19 上线日期:2014-11-03
作者简介:王卫国（1978—），男，讲师
基金资助:
国家自然科学基金项目（51205405）

Gearbox Vibration Signal Fault Feature Extraction Based on Ensemble Empirical Mode Decomposition andChoi-Williams Distribution

WANG Wei-guo^1,2, SUN Lei³

(1.School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China;2.Research Department, Ordnance Engineering College, Shijiazhuang 050003, Hebei, China;3.Army Aviation Research Institude, Beijing 100121, China)

Received:2013-11-19 Revised:2013-11-19 Online:2014-11-03

摘要/Abstract

摘要： 为实现齿轮箱故障特征提取，提出一种基于集成经验模态分解（EEMD）和乔-威廉姆斯分布（CWD）的齿轮箱振动信号特征的提取方法。对现场采集的振动信号进行EEMD分解，再对分解得到的固有模态函数（IMF）分量依照峭度准则进行排序，选取峭度指标较大的IMF分量进行CWD分析，最终得到信号的CWD.该方法可以有效抑制由于干扰项引起的频率混叠和干扰问题，有助于将原始信号在时间历程、频率成分和幅值大小3个方面的特征信息同时进行准确提取。利用该方法对实际齿轮发生断齿、裂纹故障进行了实验分析，结果表明：该方法能够全面、有效地提取齿轮振动信号中所蕴含的齿轮箱状态信息，为后续进行齿轮箱状态识别和故障诊断奠定基础。

关键词: 兵器科学与技术, 齿轮箱, 集成经验模态分解, 峭度, 乔-威廉姆斯分布, 特征提取

Abstract: For gearbox fault feature extraction, a novel method based on ensemble empirical mode decomposition and Choi-Williams distribution for gearbox vibration signal extraction is proposed. Firstly, vibration data are decomposed into several intrinsic mode functions (IMF) with EEMD, and IMFs are sorted by kurtosis criterion, then CWD is applied to the selected IMF which kurtosis is larger than others, the Choi-Williams distribution features in time, frequency and amplitude domains of the original signal can be extracted. On the basis of discussing teeth break and crack vibration fault mechanism of gearbox, the proposed method is used to analyze the vibration signal of the actual fault gearbox. The result shows that this method can efficiently extract the fault information and have great importance for condition recognition and fault diagnosis of gearbox.

Key words: ordnance science and technology, gearbox, ensemble empirical mode decomposition, kurtosis, Choi-Williams distribution, feature extraction

中图分类号:

TH17

王卫国，孙磊. 基于EEMD-CWD的齿轮箱振动信号故障特征提取[J]. 兵工学报, 2014, 35(8): 1288-1294.

WANG Wei-guo, SUN Lei. Gearbox Vibration Signal Fault Feature Extraction Based on Ensemble Empirical Mode Decomposition andChoi-Williams Distribution[J]. Acta Armamentarii, 2014, 35(8): 1288-1294.

参考文献

［1］薛松．时频分析方法在齿轮箱故障特征提取中的研究应用［D］．太原：太原理工大学，2008．
XUE Song. Time frequency analysis method applied to gearbox fault feature extraction［D］. Taiyuan: Taiyuan University of Technology, 2008. (in Chinese)
［2］张玲玲，赵懿冠，肖云魁, 等．基于小波包-AR谱的变速器轴承故障特征提取［J］．振动、测试与诊断，2011，31（4）：492-495．
ZHANG Ling-ling, ZHAO Yi-guan, XIAO Yun-kui,et al. Fault feature extraction of bearing transmission based on wavelet packet-AR spectrum［J］. Journal of Vibration Measurement and Diagnosis, 2011,31(4): 492-495. (in Chinese)
［3］张德祥，吴小培，卢一相．EMD在汽车变速齿轮箱振动故障分析的应用［J］．安徽大学学报：自然科学版，2009，33（2）：35-38．
ZHANG De-xiang, WU Xiao-pei, LU Yi-xiang. Application of EMD in fault analysis of qutomobile gearbox vibration［J］. Journal of Anhui University: Natural Science Edition, 2009,33(2):35-38. (in Chinese)
［4］李辉，郑海起，唐力伟．基于EMD和功率谱的齿轮故障诊断研究［J］．振动与冲击，2006，25(1)：133-135．
LI Hui, ZHENG Hai-qi, TANG Li-wei. Study on the gear fault diagnosis based on EMD and power spectrum analysis［J］. Journal of Vibration and Shock, 2006, 25(1):133-135. (in Chinese)
［5］高昌鑫，荆双喜．基于EEMD的齿轮箱故障诊断［J］．煤炭技术，2010，29(6):20-22.
GAO Chang-xin, JING Shuang-xi. Gear box fault diagnosis based on EEMD［J］. Coal Technology, 2010, 29(6):20-22. (in

Chinese)
［6］徐长航，刘吉飞，陈国明，等．经验模态分解和魏格纳-维利分布在往复泵泵阀振动信号特征提取中的应用［J］．中国石油大学学报：自然科学版，2010，34（3）：99-103．
XU Chang-hang, LIU Ji-fei, CHEN Guo-ming, et al. Application of EMD and WVD to feature extraction from vibration signal of reciprocating pump valves［J］. Journal of China University of Petroleum: Natural Science Edition, 2010,34（3）:99-103. (in Chinese)
［7］ Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis［J］. Proceedings the Royal Socciety of London, Series A, 1998,454(97): 903-995.
［8］ 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):31-41.
［9］张佳芳．基于EEMD的车内语音增强研究［D］．杭州：浙江大学，2007．
ZHANG Jia-fang. Study on the enhancement of car speech based on EEMD［D］. Hangzhou: Zhejiang University, 2007. (in Chinese)
［10］胡广书．现代信号处理教程［M］．北京：清华大学出版社，2005:87-93．
HU Guang-shu. Modern signal processing tutorial［M］. Beijing:Tsinghua University Press, 2005:87-93. (in Chinese)
［11］丁康，李巍华，朱小勇．齿轮及齿轮箱故障诊断实用技术［M］．北京：机械工业出版社，2005:98-101．
DING Kang, LI Wei-hua, ZHU Xiao-yong. Practical technology of gear and gearbox fault diagnosis［M］. Beijing: China Machine Press, 2005:98-101. (in Chinese)

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基于EEMD-CWD的齿轮箱振动信号故障特征提取

Gearbox Vibration Signal Fault Feature Extraction Based on Ensemble Empirical Mode Decomposition andChoi-Williams Distribution

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