Özet:

According to the nonstationary characteristics of rolling element bearing fault vibration signal, a fault diagnosis method of rolling element bearings based on Complementary Ensemble Empirical Mode Decomposition and support machine vector is proposed. The method consists of three stages. Firstly, CEEMD is used to decompose the rolling element bearings signal into several IMFs. Then, the IMF components containing main fault information was selected for constructing the faulty characteristic vector. Secondly, PCA is used to reduce the feature vector dimensions. Finally, the GA-optimized SVM is employed for rolling element bearings fault diagnosis. The presented method is applied to the fault diagnosis of rolling element bearings, and testing results show that the GA-optimized SVM can reliably separate different fault conditions, which has a better classification performance compared to the BP neural networks.

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