Abstract
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.
Highlights
The major electric machine faults include bearing defects, stator faults, broken rotor bar and end ring, and eccentricity-related faults
It can be seen that the best total accuracy (%) is up to 100 % in Table 8, it is same as the 100 % by using ensemble empirical mode decomposition (EEMD)-base-scale entropy (BSE)/permutation entropy (PE)/sample entropy (SE)/fuzzy entropy (FE)-clustering by fast search (CFS) models, but the CFS algorithm which did not require pre-set the number of clustering centers, the cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities
A method based on EEMD, BSE and CFS for roller bearings is presented in this paper, the roller bearings vibration signals are decomposed into several intrinsic mode functions (IMFs), the correlation coefficient method was is to verify the correlation degree of IMFs and the corresponding original signal
Summary
The major electric machine faults include bearing defects, stator faults, broken rotor bar and end ring, and eccentricity-related faults. A method named base-scale entropy (BSE) proposed in reference [16] It did not like the PE/FE/SE methods which need complicated sorting operation, the BSE makes only use all the adjacent points by using the root mean square in -dimensional vector to calculating the base-scale(BS) value [16], this method is simplicity and extremely fast calculation to short data sets, it was applied in physiological signal processing [17]. After extracting the feature parameters with EEMD and BSE, naturally, a classifier is expected to achieve the rolling bearing fault diagnosis automatically, such as support vector machine (SVM) [23] with particle swarm optimization (PSO) algorithm [24] and neural network (NN), label data sets are assumed available.
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