Abstract:Abstract: In order to effectively recognize the bearing running state, so as to estimate and forecast the bearing’s service life, a new method of bearing running state recognition based on non-extensive wavelet feature scale entropy and Morlet wavelet kernel support vector machine(MWSVM) was proposed. The gathered vibration signals of bearing were decomposed by the wavelet, and the corresponding wavelet coefficients were got. Based on the integration of non-extensive entropy theory and the wavelet coefficients, the wavelet feature scale entropy feature extraction method was got. But the features got through the wavelet feature scale entropy feature extraction method have the problems of high dimension and redundancy serious. Therefore, the manifold learning dimension reduction algorithm locality preserving projection was introduced to extract the characteristic features and reduce the interference of human factor. The characteristic features were inputted to the MWSVM to train and construct the bearing running state identification modal, so as to realize the bearing running state identification. The running states of a normal inner race and several inner races with different degree of fault were recognized through the proposed method, the results validate the effectiveness of the proposed algorithm.