A Gear Fault Diagnosis Method Based on EEMD Cloud Model and PSO_SVM

Abstract

Aiming at the difficulty in identifying small fault of gear, a gear diagnosis method was proposed based on integrated empirical mode decomposition (EEMD), cloud model, support vector machine, and particle swarm optimization (PSO-SVM). Firstly, the vibration signal was decomposed into several IMF components by EEMD, and the backward cloud generator calculation was performed on the IMF components to obtain the digital characteristics of the cloud model. Then, the digital features obtained and the frequency domain features and time-domain features obtained after linear reconstruction were constructed as feature vectors, which were dimensionalized by principal component analysis. Finally, the features after dimensionality reduction are input into PSO-SVM for classification training and testing. The results show that this method can effectively complete gear fault diagnosis and has a higher recognition rate.