Fault classification of rolling bearing based on hypersphere spherical center distance multiclass support vector machine

In order to reduce the training time and improve the classification accuracy of fault intelligent classification of the rolling bearing, a multi-condition classification method for normal, inner raceway fault, outer raceway fault and different fault degrees of the rolling bearing is proposed. At first, the intrinsic mode function (IMF) components are obtained by ensemble empirical mode decomposition (EEMD), and the IMFs that containing main condition information are determined by using kurtosis combined with correlation coefficient method. Then, the feature matrix is constructed with the selected IMFs, the singular values obtained by using singular value decomposition (SVD) are regarded as the feature vector. At last, when hypersphere multiclass support vector machine with optimized classification rules is used for classifying, the maximum values of each condition hypersphere spherical center distance are proposed to determine kernel parameter selection range of the multiclass classifier, narrow the selection range, and achieve multi-condition classification of the rolling bearing. The experimental results show that the training time of the classifier can be reduced and the classification accuracy can be improved by the proposed multi-condition classification method of the rolling bearing. © 2013 Chinese Optics Letters.