基于EMD模糊熵和SVM的转子系统故障诊断

提出一种经验模态分解、模糊熵和支持向量机相结合的转子系统故障诊断方法。该方法首先对转子系统故障信号进行经验模态分解，得到若干阶表征故障信息的固有模态函数，并运用基于能量原理的虚假模态消除方法剔除虚假模态分量；再利用模糊熵能够表示信号复杂程度且具有相对稳定性等特点，选取前4阶固有模态函数的模糊熵值作为各故障信号的特征向量；最后将该特征向量输入到支持向量机中进行转子系统的故障分类。试验结果表明，该方法能够有效的提取转子系统故障特征和对转子系统进行故障诊断。

Fault Diagnosis of Rotor System Based on EMD-Fuzzy Entropy and SVM

A comprehensive fault diagnosis method combining Empirical Mode Decomposition (EMD) with Fuzzy Entropy and Support Vector Machine (SVM) for rotor system was proposed. Firstly, the fault signal of the rotor system was decomposed with EMD method into a number of intrinsic mode functions (IMFs), and a method for canceling pseudo mode function in EMD was presented based on energy conservation law. Then as the fact that Fuzzy Entropy can express the complexity of signal and has relative stability, the fuzzy entropies of the first four IMFs were taken as eigenvectors of fault signals. Finally, the eigenvectors were put into SVM to distinguish the faults of the rotor system. The result indicates that this method can effectively extract fault characteristics and diagnose the fault of the rotor system.