基于能量聚集度经验小波变换的齿轮箱早期微弱故障诊断  

Early Weak Fault Diagnosis of Gearboxes Based on Energy Aggregation and EWT

作　　者：王友仁[1] 陈伟[1] 孙灿飞[1] 孙权[1] 黄海安[1] 

机构地区：[1]南京航空航天大学自动化学院,南京211106

出　　处：《中国机械工程》2017年第12期1484-1490,共7页China Mechanical Engineering

基　　金：国家商用飞机制造工程技术研究中心创新基金资助项目(SAMC14-JS-15-01);航空科学基金资助项目(2013ZD52055)

摘　　要：齿轮箱早期故障的故障特征不明显,振动信号呈现出强烈的非线性、非平稳现象,为此,提出了一种基于能量聚集度经验小波变换(EA-EWT)的齿轮箱故障诊断方法。首先对采集的振动信号进行EA-EWT分解,对分解后的各层信号采用最大峭度-包络谱熵准则进行敏感分量筛选,再利用最小熵解卷积对筛选出的分量信号进行降噪处理,对降噪后信号进行Hilbert包络谱分析,通过包络谱中的频率成分识别出故障类型,实现早期故障诊断。试验结果表明,该方法能够明显增强早期微弱故障特征,提高齿轮箱早期故障诊断性能。For the early faults of gearboxes, the fault features were not obvious, and the vibration signals were nonlinear and non-stationary, a method was proposed based on energy aggregation and EWT （EA-EWT）. Vibration signals were decomposed by EA-EWT, and the maximum kurtosis envelope spectrum entropy criterion was used to filter the sensitive signals. For the selected signals, the minimum entropy deconvolution was used to reduce the noise. The Hilbert envelope spectrum of the signals was analyzed after noise reduction, and the fault types were identified by the frequency compo- nents in the envelope spectrum to realize the early fault diagnosis. The experimental results show that the new method may significantly enhance the early weak fault characteristics and improve the early fault diagnosis performances.

关 键 词：经验小波变换 最大峭度-包络谱熵 齿轮箱 故障诊断 

分 类 号：TH113[机械工程—机械设计及理论]