Aiming at the problems of nonlinearity

non-stationarity and difficult identification of rolling bearing vibration signals

a fault identification method for rolling bearings based on empirical wavelet transform（ EWT）

singular value entropy and t-distributed stochastic neighbor embedding（ t-SNE） is proposed. The method performs EWT decomposition on the original vibration signal to obtain several intrinsic modal function（ IMF）

and performs singular value decomposition on the IMF to obtain singular value entropy. The t-SNE algorithm is used to reduce the feature matrix composed of singular value entropy

and the extracted low-dimensional features can effectively reflect the fault information. Finally

low-dimensional features are input into the K-means classifier for pattern recognition. The method is applied to rolling bearing experiments and compared with EMD + singular value entropy + t-SNE

EWT + singular value entropy + PCA method. The results show that the method can extract the fault characteristics of rolling bearings more effectively and improve the accuracy of fault identification.