基于EEMD能量熵和支持向量机的齿轮故障诊断方法

摘要

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 ensemble empirical mode decomposition (EEMD) energy entropy and support vector machine is put forward. 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 can identify gear fault patterns effectively.%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,提出基于总体平均经验模态分解(EEMD)和支持向量机的齿轮故障诊断方法.通过EEMD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(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,故可通过计算不同振动信号的EEMD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,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.