针对齿轮振动信号的非平稳特征和现实中难以获得大量典型故障样本的实际情况，提出了基于经验模态分解（empirical mode decomposition，EMD)和支持向量机的齿轮故障诊断方法。首先通过EMD方法将非平稳的原始加速度振动信号分解成若干个平稳的本征模函数(intrinsic mode function, IMF)；齿轮发生不同的故障时，在不同频带内的信号能量值会发生改变，故可以通过计算不同振动信号的EMD能量熵判断是否发生故障；从包含有主要故障信息的IMF分量中提取出来的能量特征作为输入建立支持向量机（support vector machine，SVM），判断齿轮的工作状态和故障类型。实验结果表明，文中提出的方法能有效地应用于齿轮的故障诊断。

In view of the non-stationary features of vibration signals of gear and the difficulty to obtain a large number of fault samples in practice, a fault diagnosis scheme based on empirical mode decomposition (EMD) energy entropy and support vector machine is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs); the energy of vibration signal will change in different frequency bands when fault occurs. Therefore, to identify the fault pattern and condition, energy feature extracted from a number of IMFs that contained the most dominant fault information could serve as input vectors of support vector machine. Practical examples show that the diagnosis approach put forward in this paper can identify gear fault patterns effectively.