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 and Choi-Williams Distribution

作　　者：王卫国[1,2] 孙磊[3] 

机构地区：[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 [3]总参谋部陆航研究所,北京100121

出　　处：《兵工学报》2014年第8期1288-1294,共7页Acta Armamentarii

基　　金：国家自然科学基金项目(51205405)

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。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.

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 

分 类 号：TH17[机械工程—机械制造及自动化]