中文摘要：

提出一种基于微分局部均值分解（Differential local mean decomposition,DLMD）的旋转机械故障诊断方法.该方法在局部均值分解（Local mean decomposition,LMD）过程中融入微分和积分运算.对原始信号进行k阶微分,然后对微分后信号进行LMD分解,对分解得到的各乘积函数（Production function,PF）分量循环进行一次积分和一阶LMD分解,直至循环k次,得到m个PF分量和残余分量,将所有PF分量的瞬时幅值和瞬时频率组合,便可以得到原始信号完整时频分布.将该方法应用于旋转机械故障诊断研究中,通过仿真和试验进行分析研究,结果表明,基于微分局部均值分解的旋转机械故障诊断方法能够有效地抑制虚假干扰频率,提高旋转机械故障诊断准确性.

英文摘要：

A rotating machinery fault diagnosis method based on differential local mean decomposition （DLMD） is proposed.The differential and integral operations are integrated into the traditional local mean decomposition （LMD）.Theoriginal signal is processed with k-order differential,and then the signal obtained is decomposed using LMD.The production function （PF） components obtained are circularly processed with an integral and first-order the LMD decomposition until k times,and it can get m PF components and the residual component.The whole time-frequency distribution of the original signal can be obtained by the combination of the instantaneous amplitude and instantaneousfrequency of all the PF components.The method is applied to rotating machinery fault diagnosis study which is analyzed by simulation and experimental study.The results show that,the fault diagnosis method of rotating machinery based on DLMD can effectively suppress the false interference frequency,and improve the accuracy of rotating machinery fault diagnosis.