Rolling Bearing Fault Diagnosis Method Based on EEMD Permutation Entropy and Fuzzy Clustering

In order to improve the precision of rolling bearing fault diagnosis, this paper puts forward a method for rolling bearing fault diagnosis based on EEMD permutation entropy and fuzzy clustering. Firstly, it sets normal and damage acoustic emission signals of rolling bearing inner ring by using EEMD algorithm, to obtain several intrinsic mode function (IMF) components, and then extracts the permutation entropy as the signal eigenvalue in sensitive IMF of reflecting signal characteristic, then it can conduct the fault identification and classification in fuzzy clustering analysis. The experimental results show that the method can be effectively applied to rolling bearing fault diagnosis, and it has higher diagnosis accuracy.