Abstract: Aiming to extract the faint vibration signal from rolling bearing and classify extracted fault features, the paper fuses together multiscale range entropy (MRE) with EigenClas, and proposes a MRE-EigenClass method to diagnose bearing fault modes. Firstly, the MRE extracts the feature vectors with 20 scales from the vibration signals of bearings in different working states. Then, the extracted feature vectors are input into EigenClass classifier to obtain the final results. The experiments show that the proposed MRE-EigenClass algorithm can effectively extract the features of vibration signals and realize high-precision classification for rolling bearings. Compared with other fault classifiers, this method has the higher fault classification accuracy(98.86%).