基于EEMD和进化支持向量机的齿轮混合智能诊断方法研究
Hybrid Intelligent Diagnosing Based on EEMD and Genetic-support Vector Machine

针对齿轮早期故障特征不明显，提出了一种基于总体平均经验模态分解（ensemble empirical mode decomposition，EEMD）和进化支持向量机相结合的齿轮故障智能诊断方法。利用EEMD能对齿轮振动信号进行自适应的分解成若干本征模式分量（intrinsic mode function，IMFs），并能有效抑制经典经验模式分解可能出现的模式混叠现象。以所得的IMF分量中提取出来的能量特征为输入建立进化支持向量机，判断齿轮的故障状态。结果表明：建立的混合智能诊断方法的分类正确率最高，能有效诊断齿轮早期故障。
Due to the incipient fault attributes of gear are not obvious, a hybrid diagnosis model for gear diagnosing based on Ensemble Empirical Mode Decomposition (EEMD) and Genetic-Support Vector Machine (GSVM) was proposed. With the EEMD method,the gear vibration signals are adaptively decomposed into a finite number of Intrinsic Mode Functions(IMF),which can alleviate mode mixing that may appear in conventional EMD method. The energy character vectors of every IMF component is calculated and the energy features extracted from a number of IMFs that contained the most dominant fault information are served as the input genetic-vectors of the support vector machine, then the fault state of the gear can be determined. The results show the high correct classification rate of the proposed method