用盲反卷积和改进谱减法提取轴承微弱特征  

Application of Blind Deconvolution and Improved Spectral Subtraction Method in Extracting Weak Feature of Rolling Bearing

作　　者：袁幸[1] 朱永生[1] 洪军[2] 张优云[1] 

机构地区：[1]西安交通大学现代设计及转子轴承系统教育部重点实验室,西安710049 [2]西安交通大学机械制造系统工程国家重点实验室,西安710049

出　　处：《振动．测试与诊断》2012年第2期202-207,339,共6页Journal of Vibration,Measurement & Diagnosis

基　　金：国家科技重大专项资助项目(编号:2009ZX04014-015;2009ZX04014-101);国家高技术研究发展计划("八六三"计划)资助项目(编号:2009AA04Z147)

摘　　要：为了有效提取滚动轴承早期损伤时微弱的故障特征,提出盲反卷积和改进谱减法(SSM)的振动信号分析方法。建立了滚动轴承振动信号卷积分析模型,阐述了冲击传递过程,根据无量纲特征构造了优化盲反卷积滤波器以检测振动信号中的微弱冲击成分。引入高效信号消噪方法——SSM消除盲反卷积后的背景噪声以增强故障特征。由于工程中轴承噪声频带较宽且幅值相差较大,易引起附加噪声分量,在经典SSM基础上,根据滚动轴承振动信号损伤信息存在于低频和高频调制区的特点,通过噪声能量和畸变量指标优化调整参数进行频域谱减。测试信号处理显示了改进SSM的优越性。最后将盲反卷积和改进SSM用于轴承诊断,结果表明该方法能提取滚动轴承早期损伤的冲击特征。In order to extract incipient weak features of rolling bearing from strong background noise,a new approach based on blind deconvolution and improved spectral subtraction method(SSM) is proposed.Firstly,a convolution model of rolling bearing vibration signal is presented.The transfer process of impulses is described,and a blind deconvolution filter is optimized to detect the impulsive components by means of the higher-order statistical(HOS) properties.Then,an effective methodology-SSM is introduced to differentiate noise from impulses to enhance the ability of features.To reduce additional noise induced by random impulse excitations,using modified parameters,an improved spectral subtraction is proposed through fault information existing in low and high frequency modulation areas.Furthermore,residual noise energy and signal distortion criterion are employed to evaluate the parameters.Test signal analysis shows the superiority of improved SSM.Finally,experimental data are used to verify the excellent reliability of blind deconvolution along with the improved SSM.

关 键 词：滚动轴承 微弱特征 盲反卷积 改进谱减法 

分 类 号：TH17[机械工程—机械制造及自动化] TH133.3