Abstract
Aiming at the problem that the composite fault vibration signal of rolling bearing is complex and it is difficult to effectively extract the impact characteristics of the composite fault, a composite fault diagnosis method of rolling bearing based on multi-scale fuzzy entropy feature fusion is proposed. Compared with traditional fault feature extraction methods that can only extract single fault feature information, this method can increase the discrimination of composite fault features, effectively separate multiple composite fault features, and more comprehensively characterize composite fault feature information. First, the signal is processed by EEMD, getting a series of IMF components. Secondly, the energy and kurtosis index of the IMF component are calculated, the appropriate IMF component is selected through the correlation coefficient to obtain a new time series, the multi-scale fuzzy entropy is calculated, and feature fusion performed. Finally, the least square support vector machine is used to diagnose the fault of the fusion feature. The method is verified by a mechanical failure simulation test bench. The experimental results show that this method can quantitatively characterize the data information of fault signal, improve the anti-interference ability, have good feature extraction ability of composite fault of rolling bearings, and can effectively identify the type of composite fault. Compared with the method using multi-scale fuzzy entropy, energy and kurtosis index alone, the accuracy of fault diagnosis increases by 8.12 % and 11.65 %, respectively.
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Yixin Zhao is a Senior Two Student of Experimental Middle School affiliated with Beijing Normal University, with excellent performance, selected into the “Talent Program” organized by China Association for Science and Technology and Ministry of Education. From 2020 to now, she has studied in the laboratory of Beijing University of Technology. Her research interests include mathematical modeling, monitoring and fault diagnosis.
Yao Fan is currently a Master’s Candidate in Control Engineering at Beijing University of Technology. Her research interests are fault diagnosis and residual life prediction of mechanical equipment.
Hu Li is currently Master’s Candidate in Control Engineering at Beijing University of Technology. His research interest is fault diagnosis of mechanical equipment.
Xuejin Gao is a Professor of the Faculty of Information Technology, Beijing University of Technology, Beijing, China. He received his Ph.D. in Pattern Recognition and Intelligent Systems from Beijing University of Technology. His research interests include complex system modeling, monitoring and fault diagnosis.
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Zhao, Y., Fan, Y., Li, H. et al. Rolling bearing composite fault diagnosis method based on EEMD fusion feature. J Mech Sci Technol 36, 4563–4570 (2022). https://doi.org/10.1007/s12206-022-0819-x
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DOI: https://doi.org/10.1007/s12206-022-0819-x