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.