基于最小熵解卷积和Teager能量算子直升机滚动轴承复合故障诊断研究  

Helicopter rolling bearing hybrid faults diagnosis using minimum entropy deconvolution and Teager energy operator

作　　者：陈海周[1] 王家序[1,2] 汤宝平[1] 李俊阳[1] 

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400044 [2]四川大学空天科学与工程学院,成都610065

出　　处：《振动与冲击》2017年第9期45-50,73,共7页Journal of Vibration and Shock

基　　金：国家自然科学基金(51375506;51475051);重庆市研究生科研创新项目(CYB16027)

摘　　要：为了解决强背景噪声环境下直升机滚动轴承故障信号微弱,故障特征难以提取的问题,提出一种基于最小熵解卷积(Minimum Entropy Deconvolution,MED)与Teager能量算子(Teager Energy Operator,TEO)的滚动轴承故障特征提取的新方法。根据滚动轴承故障信号表现为冲击波形的特点和MED降噪对冲击特征敏感的特性,采用MED对故障信号进行降噪处理,同时增强信号中的冲击成分;再结合TEO适合检测信号的瞬时变化,能有效提取故障信号冲击特征的特点,计算降噪信号的Teager能量信号,进行频谱分析提取滚动轴承的故障特征。通过对仿真信号和直升机滚动轴承混合故障信号进行分析,实验结果表明,该方法能有效提取强背景噪声环境中的微弱复合故障特征,具有一定的工程应用价值。In order to solve the problem that fault signals of helicopter rolling bearing are weak and fault characteristics are difficult to extract under strong background noise, a new method based on the minimum entropy deconvolution (MED) and Teager energy operator was proposed to extract fault characteristics of rolling bearings. According to impulse characteristics of rolling bearing fault signals and the feature that MED is sensitive to impulse characteristics, the MED was firstly used to denoise fault signals and enhance impulse components. Teager energy operator is suitable for instantaneous change of detected signals and can effectively extract impact characteristics of fault signals. Teager energy signals of the above denoised fault signals were computed, then fault features of rolling bearings were extracted with the spectral analysis of Teager energy signals. The proposed method was validated by analyzing simulated signals and hybrid fault signals of helicopter rolling bearings. The test results demonstrated that the proposed method can effectively extract weak and hybrid fault features under strong background noise, and have a certain engineering application value. © 2017, Editorial Office of Journal of Vibration and Shock. All right reserved.

关 键 词：直升机 滚动轴承 最小熵解卷积 TEAGER能量算子 故障诊断 

分 类 号：TH133.3[机械工程—机械制造及自动化]