 This paper proposes a new approach for bearing fault diagnosis using multi-scale permutation entropy, MP, which is more effective than existing methods such as single-scale permutation entropy, PE, and multi-scale entropy, MSE. The MP algorithm extracts features from vibrations in order to classify them into normal or faulty conditions. Then, support vector machine SVM is used to identify the fault type with high accuracy. This method outperforms other approaches in terms of accuracy and robustness. This article was authored by Jianjian Ding, Chen Qihuan, Qiwen Wu, and others.