关键词: 滚动轴承, 支持向量机, 故障诊断, 双树复小波包, 人工鱼群算法

Abstract:

In order to improve the fault diagnosis efficiency of the inner ring, rolling element and outer ring of rolling bearings, a method of fault diagnosis is proposed based on dual-tree complex wavelet packet transform and Support Vector Machine（SVM）. The vibration signal is decomposed and reconstructed through dual-tree complex wavelet packet. The energy features are extracted in the reconstructed signal and taken as input of the support vector machine. The parameters of the support vector machine are not easy to determine, which reduces the accuracy of fault diagnosis, so artificial fish swarm algorithm is employed to optimize the coefficients. The better support vector machine is used to identify the fault type of rolling bearings. The experimental results prove that the de-noising effect of the proposed method is better than the traditional method and it can extract fault characteristic effectively, which can improve the accuracy of fault diagnosis.

Key words: roller bearings, Support Vector Machine（SVM）, fault diagnosis, dual-tree complex wavelet package, artificial fish swarm algorithm