关键词: 集合经验模态分解, 遗传算法, 支持向量机, 滚动轴承, 退化状态, 故障诊断

Abstract: To recognize the degradation state of rolling bearing accurately,a hybrid method by integrating Ensemble Empirical Mode Decomposition (EEMD) and Support Vector Machine (SVM) was proposed,and the model for degradation state recognition of rolling bearing was constructed.The original signal was decomposed into many components with EEMD adaptively,and adaptive reconstruction was performed by using the correlation coefficient method to eliminate noise and fault diagnosis.The feature vectors of degradation state were extracted through the combination of Genetic Algorithm (GA) and SVM.The degradation state probability distribution and historical remnant life of rolling bearing were calculated to determine the optimal number of degradation state,which was employed to construct SVM model for degradation state recognition.The analytical results for full lifetime datasets of a certain bearing demonstrated the validity of the method.

Key words: ensemble empirical mode decomposition, genetic algorithms, support vector machine, rolling bearing, degradation state, fault diagnosis