 The proposed method uses improved local mean decomposition, LMD, multi-scale permutation entropy, MP, and hidden Markov model, HMM, to diagnose the faults of rolling bearings. It first decomposes the vibration signal into multiple components, then calculates the phase space reconstruction of one component, and sets the delay time and embedding dimension to determine the scale. Next, it extracts the features of the multi-scale permutation entropy, MP, and uses them as HMM training and diagnosis. Finally, the proposed method successfully identifies the different faults of the rolling bearing. This article was authored by Yang DeGao, Francesco Vallejo, Mingli, and others.