Online identification of rolling bearing degradation state based on DSHDD and fuzzy evaluation

被引：0
|
作者：
Zhou J.-M. [1 ,2 ]
Yin W.-H. [1 ,2 ]
You T. [1 ,2 ]
Zhang L. [1 ,2 ]
Wang F.-L. [1 ,2 ]
Yu J.-C. [1 ,2 ]
机构：
[1] School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang
[2] Key Laboratory of Conveyance and Equipment of Ministry of Education, Nanchang
来源：
Zhendong Gongcheng Xuebao/Journal of Vibration Engineering | 2021年 / 34卷 / 03期
关键词：
Description of double hypersphere data domain; Fault diagnosis; Performance degradation assessment; Rolling bearing; Set empirical mode decomposition;
D O I：
10.16385/j.cnki.issn.1004-4523.2021.03.023
中图分类号：
学科分类号：
摘要：
In the long-term use process, the performance of rolling bearing will be degraded to different degrees. If the degradation state of rolling bearing can be identified online, accidents can be effectively prevented. In this paper, an adaptive noise-assisted collective empirical mode decomposition (CEEMDAN) method combined with energy entropy is proposed to extract the characteristics of vibration signals, and then the characteristics are input into the DSHDD model, and the obtained results are input into the membership function to calculate the membership, which can be used as the evaluation index of performance degradation. An adaptive threshold is set using 3σ to determine the bearing's early failure threshold. CEEMDAN and Hilbert envelope demodulation methods are used to verify the correctness of the evaluation results. The validity and practicability of the model are verified by using the bearing life cycle data from the University of Cincinnati. © 2021, Editorial Board of Journal of Vibration Engineering. All right reserved.
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页码：646 / 653
页数：7
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