基于EEMD云模型与SVM的汽轮机转子故障诊断方法

A rotor fault diagnosis method based on EEMD cloud model and SVM

提出了一种基于集合经验模态分解(EEMD)、云模型与支持向量机(SVM)相结合的汽轮机转子多故障诊断方法。该方法首先采用EEMD将振动信号分解成若干个IMF分量,利用相关系数法对IMF分量进行筛选,然后对筛选后的IMF分量进行逆向云发生器计算,得到云模型的数字特征并构建为特征向量,将其应用到有向无环图SVM中进行转子多故障状态识别,并与传统的EEMD能量法进行对比。结果表明,该方法能够准确地完成转子多故障诊断,具有更高的识别率。

A multi-fault diagnosis method for steam rotors based on ensemble empirical mode decomposition （EEMD）, cloud model and support vector machine （SVM） was proposed. This method firstly uses the EEMD to decompose the vibration signals into several IMF components and the correlation coefficient method to screen the IMF components. Then, it applies the backward cloud generator algorithm to compute the screened IMF components and obtains digital characteristics of the cloud model to build the feature vector. Finally, the feature vector is applied to the directed acyclic graph SVM to perform multi-fault state recognition for the rotors, and the result is compared with that of the conventional EEMD energy method. The experimental results show that, this method can accurately complete multi-fault diagnosis for the rotors and has more accurate recognition rate.