最小周期相关熵解卷积结合窄带解调的轴承复合故障诊断研究  

The research of bearing multi-fault diagnosis based on minimum period correlated entropy deconvolution and narrowband demodulation

作　　者：张晓涛[1] 唐力伟[1] 王平[1] 邓士杰[1] 

机构地区：[1]军械工程学院火炮工程系,河北石家庄050003

出　　处：《振动工程学报》2015年第4期666-672,共7页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(50775219);军队科研资助项目([2011]107)

摘　　要：声发射检测齿轮箱故障灵敏度高,但故障信号具有高频宽带且噪声干扰严重的特点,针对齿轮箱轴承复合故障声发射信号处理问题,提出最小周期相关熵解卷积与窄带解调相结合的复合故障诊断方法,基于故障出现的周期信息,利用最小周期相关熵解卷积实现故障信号分离,通过窄带解调方法获得最优解调中心频率,抑制宽频带解调引入的噪声干扰,仿真和实验数据处理结果表明:此方法适宜处理轴承复合故障声发射信号,成功实现了复合故障诊断。Acoustic emission is a sensitive detection method for rolling bearing fault of gearbox,but the fault signal contains strong noise and has the characteristics of high frequency and wide bandwidth.Aiming at the multi-fault signal processing problems of rolling bearing,a novel multi-fault diagnosis approach of minimum period correlated entropy deconvolution and narrowband demodulation was presented.According to fault period information,minimum period correlated entropy deconvolution can separate fault signal,narrowband demodulation can obtain the optimal center frequency and reduce the noise caused by wideband demodulation.The result of simulation and testing data show that the method of minimum period correlated entropy deconvolution and narrowband demodulation was suitable for bearing multi-fault signal processing,and achieved the fault diagnosis successfully.

关 键 词：故障诊断 声发射 最小周期相关熵解卷积 窄带解调 复合故障 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911.72[电子电信—通信与信息系统]