基于经验模态分解和支持向量机的滚动轴承故障诊断

In this paper, an adaptive waveform matching method is proposed to improve the end effect of empirical mode decomposition(EMD).Then a two-phase fault diagnosis method for rolling bearing is presented based on improved EMD and Particle Swarm Optimization(PSO)optimized support vector machine(SVM).In the of fline phase, the typical normal and fault vibration signals are decomposed by IEMD and energy information is extracted as the feature.A PSO-SVM model is trained and saved as diagnostic model.In the online phase, the real-time vibration signal is decomposed by IEMD and the feature is extracted.The model trained in of fline phase executes diagnostic process and output the diagnosis results.The method is veri fied using Case Western bearing datasets.The experimental results show the effectiveness of the method in fault diagnosis of rolling bearing.