基于内禀模态能量熵与支持向量机的转子故障智能诊断方法的研究

摘要

大型旋转机械转子的运转情况是生产过程中最重要的问题之一,在故障初期对故障识别并实现智能诊断具有重要的意义.然而大型旋转机械存在较大的非线性,并且故障样本较少,给特征提取和状态识别带来了很大困难.基于经验模态分解(EMD)后内禀模态函数的能量嫡,提取各个内禀模态函数的能量作为特征向量,并以此作为支持向量机(SVM)的输入参数来输入支持向量机进行故障诊断.实验表明这种方法能够对故障状态与正常状态正确分类,实现故障的智能诊断.%The large rotating machinery functioning of the rotor is one of the most important issues. It has great significance to identify the fault early and implement intelligent fault diagnosis. However there is a big nonlinear about large rotating machinery and has less fault samples. This cause big difficult for feature extraction and state recognition. Based on empirical mode decomposition entropy, we extract each intrinsic mode function energy as eigenvector and make them for input parameter of the support vector machine ( SVM) to fault diagnonisis. The experiment shows that this method can classify the fault state and the normal state,and completed intelligent fault diagnosis.