改进经验小波变换与最小熵解卷积在铁路轴承故障诊断中的应用  

Application of improved wavelet transform and minimum entropy deconvolution in railway bearing fault diagnosis

作　　者：乔志城 刘永强 廖英英[2,3] QIAO Zhicheng;LIU Yongqiang;LIAO Yingying(School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;School of Civil Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)

机构地区：[1]石家庄铁道大学机械工程学院,石家庄050043 [2]石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室(筹),石家庄050043 [3]石家庄铁道大学土木工程学院,石家庄050043

出　　处：《振动与冲击》2021年第2期81-90,118,共11页Journal of Vibration and Shock

基　　金：国家自然科学基金(11790282,11572206,U1534204,11802184);河北省自然科学基金(A2016210099);河北省人才工程培养经费资助科研项目(A2016002036);河北省高等学校高层次人才科学研究项目(GCC2014021)。

摘　　要：经验小波变换是一种小波框架下的自适应信号分解方法,对旋转机械的非线性、非平稳振动信号有很好的分解作用。针对传统经验小波变换过程中频谱划分过多的问题,提出根据互信息值对频谱进行重新划分与合并的方法,能有效减少频带数量;选择峭度值最大的分量进行信号重构,再使用最小熵解卷积对重构信号进行降噪;对降噪后的信号进行包络分析,能够有效地诊断出滚动轴承的微弱故障。通过仿真信号与铁路货车轮对轴承实验信号验证了该方法的有效性,为下一步工程应用奠定了基础。Empirical wavelet transform is an adaptive signal decomposition method in wavelet framework,which can decompose nonlinear and non-stationary vibration signals of the rotating machinery.Aiming at the problem of too much spectrum division in the process of traditional empirical wavelet transform,a new method of spectrum division and combination based on mutual information value was proposed,which can effectively reduce the number of frequency bands.Then,the component with the maximum kurtosis value was selected for signal reconstruction,and the minimum entropy deconvolution was used to reduce the noise of the reconstructed signal.The signal envelope analysis after noise reduction can effectively diagnose the weak fault of rolling bearings.Simulation signals and experimental signals were used to verify the method in the paper,which lays a foundation for further engineering applications.

关 键 词：经验小波变换 互信息 最小熵解卷积 包络分析 

分 类 号：TH133.3[机械工程—机械制造及自动化]