Fault diagnosis of rolling bearing based on improved EEMD and convolutional neural network
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Abstract:
EEMD(ensemble empirical mode decomposition)is an analysis method for signal decomposition.However,there is serious divergence in the two endpoints of its modal function (IMF). If the decomposition results are directly applied to the fault diagnosis system, the diagnosis accuracy will decrease. In the paper, support vector machine (SVM) and EEMD algorithm were combined to decompose signal and the reliability analysis was conducted with simulation signal. After selecting the components of SVM-EEMD decomposition, the signal was decomposed further and the energy vector was constructed. Finally, with a combination of SVM-EEMD and convolutional neural network, rolling bearing fault diagnosis model was constructed and verified by experiment.The experimental comparison results show that the improved EEMD algorithm can effectively solve the problem of the endpoints divergence, and the fault diagnosis model constructed improves the fault diagnosis accuracy.
