基于改进的辛周期模态分解的滚动轴承复合故障诊断方法  

Compound fault diagnosis method for rolling bearings based on the improved symplectic period mode decomposition

作　　者：刘敏 程军圣[1,2] 谢小平 吴占涛[1] LIU Min;CHENG Junsheng;XIE Xiaoping;WU Zhantao(School of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China;Shenzhen Research Institute,Hunan University,Shenzhen 518000,China)

机构地区：[1]湖南大学机械与运载工程学院,长沙410082 [2]湖南大学深圳研究院,广东深圳518000

出　　处：《振动与冲击》2024年第14期47-56,共10页Journal of Vibration and Shock

基　　金：国家重点研发计划(2021YFF0603000);国家自然科学基金(52275103);深圳自然科学基金(JCYJ20210324115413036)。

摘　　要：辛周期模态分解(symplectic period mode decomposition, SPMD)方法可以准确地提取周期脉冲分量,是一种有效的滚动轴承单一故障诊断方法。但在滚动轴承出现复合故障时,尤其是强背景噪声下,周期脉冲信号往往较微弱,使得SPMD难以提取出不同周期的脉冲分量,进而限制了其在复合故障诊断中的应用。对此,提出了改进的辛周期模态分解(improved symplectic period mode decomposition, ISPMD)方法。该方法首先采用求差增强技术和最小噪声幅值反卷积相结合的方法对信号进行降噪,增强周期脉冲,以准确估计故障周期;然后构造对应的周期截断矩阵,并通过辛几何相似变换和周期冲击强度获得辛几何周期分量;最后对残差信号采用迭代分解,进而得到不同周期的辛几何周期分量。试验结果表明,ISPMD能准确提取出周期脉冲分量,是一种有效的滚动轴承复合故障诊断方法。The symplectic period mode decomposition(SPMD)method can accurately extract the periodic pulse components in a signal,which is an effective method for the single fault diagnosis of rolling bearings.However,in the case of composite faults in rolling bearings,especially under strong background noise,the periodic pulse signals are often weak,which makes it difficult to extract the pulse components with different periods,thus limiting its application in the diagnosis of composite faults.An improved symplectic period mode decomposition(ISPMD)method was proposed to deal with this regard.The method firstly adopts the combination of the strengthen operate subtract operate enhancement technique and minimum noise amplitude deconvolution method to reduce the noise in the signal and enhance the period pulse to accurately estimate the fault period.Then,the periodic segment matrix was constructed and the symplectic geometry period component was obtained by the symplectic geometry similarity transformation and the periodic impact intensity.Finally,the residual signal was decomposed by iteration and the symplectic geometry period components with different periods were obtained.The experimental results show that ISPMD can accurately extract the periodic impulse components,which is an effective method for composite fault diagnosis of rolling bearings.

关 键 词：改进的辛周期模态分解(ISPMD) 求差增强技术最小噪声幅值反卷积 滚动轴承 复合故障诊断 

分 类 号：TN911.7[电子电信—通信与信息系统] TH165.3[电子电信—信息与通信工程]