A fault diagnosis method combined with ensemble empirical mode decomposition, base-scale entropy and clustering by fast search algorithm for roller bearings

A method based on ensemble empirical mode decomposition (EEMD), base-scale entropy (BSE) and clustering by fast search (CFS) algorithm for roller bearings faults diagnosis is presented in this study. Firstly, the different vibration signals were decomposed into a number of intrinsic mode functions (IMFs) by using EEMD method, then the correlation coefficient method was used to verify the correlation degree between each IMF and the corresponding original signals. Secondly, the first two IMF components were selected according to the value of correlation coefficient, each IMF entropy values was calculated by BSE, permutation entropy (PE), fuzzy entropy (FE) and sample entropy (SE) methods. Thirdly, comparing the elapsed time of BSE/PE/FE/SE models, using the first two IMF-BSE/PE/FE/SE entropy values as the input of CFS clustering algorithm. The CFS clustering algorithm did not require pre-set the number of clustering centers, the cluster centers were characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. Finally, the experiment results show that the computational efficiency of BSE model is faster than that of PE/FE/SE models under the same fault recognition accuracy rate, then the effect of fault recognition for roller bearings is good by using CFS method.