中文摘要：

研究了一种新的自适应时频分析方法——局部均值分解LMD（Local mean decomposition）方法，并针对齿轮故障振动信号的调制特征，提出了基于LMD的齿轮故障诊断方法。LMD方法可以自适应地将任何一个复杂信号分解为若干个瞬时频率具有物理意义的PF（Product function）分量之和，从而获得原始信号完整的时频分布，其本质上是将多分量的信号自适应地分解为若干个单分量的调幅一调频信号之和，非常适合于处理多分量的调幅一调频信号。在介绍LMD方法的基础上，对LMD和EMD（Empirical mode decomposition）方法进行了对比，结果表明了LMD方法的优越性，同时将LMD方法应用于齿轮故障诊断，对实际的齿轮故障振动信号进行了分析，结果表明LMD方法可以有效地应用于齿轮故障诊断。

英文摘要：

A newly self-adaptive time-frequency analysis method, the local mean decomposition （LMD） is studied in the paper. Aiming at the modulated features of the gear fault vibration signal, the gear fault diagnosis method based on LMI） is proposed. By using LMD, any complicated signal can be decomposed into a number of product functions whose instantaneous frequencies own physical sense and the integrity time-frequency distribution of the original signal can be obtained. In nature, the multi-component signal can be decomposed into a number of single-component amplitude-modulated and frequency-modulated signals by LMD. In this paper, the LMD method is introduced, then compared with EMD method and the results show the superiority of the LMD method. Furthermore, the LMD method is applied to the gear fault diagnosis. The analysis results from the actual gear fault vibration signal demonstrate that the LMD method can be applied to the gear fault diagnosis effectively.