Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine

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 method for feature extraction was provided.But,the features got by the method are of high dimension and serious redundancy.Therefore,for dimension reduction,the manifold learning algorithm with locality preserving projection was introduced to extract the characteristic features and reduce the interference of human factors.The characteristic features were input to the MWSVM to train and construct an identification model,so as to realize the bearing running state identification.The running states of one 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.