Rolling bearing life prediction method based on improved similarity theory

被引：0

作者：

Cui L.-L. ^{[1
]}

Jin O. ^{[1
]}

Wang X. ^{[1
]}

机构：

[1] Beijing Key Laboratory of Advanced Manufacturing Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing

来源：

Zhendong Gongcheng Xuebao/Journal of Vibration Engineering | 2023年 / 36卷 / 03期

关键词：

double exponential function; fault diagnosis; parameter similarity; remaining life prediction; rolling bearing;

D O I：

10.16385/j.cnki.issn.1004-4523.2023.03.028

中图分类号：

学科分类号：

摘要：

Traditional similarity life prediction methods ignore the local evolution characteristics of degradation process，which leads to low prediction accuracy. Traditional characteristic indexes in time and frequency domain are difficult to realize early fault monitoring，and local fluctuation is large in the later stage of degradation. The trend fitting strategy of Gaussian function is introduced and an improved similarity matching optimization method is proposed. A Jensen-Renyi divergence health index based on Gaussian mixture model is proposed to accurately track the evolution trend of rolling bearing degradation. Since it is difficult to obtain a large number of degradation signals in real life cycle，a double exponential function model is constructed to simulate degradation signals and verify the validity of the simulation data to expand the reference dictionary set. Gaussian function is used to fit the degradation data and parameter similarity principle is proposed to predict the remaining service life. The experimental results of rolling bearing life cycle degradation verify that the proposed method can effectively improve the prediction accuracy of residual life. © 2023 Nanjing University of Aeronautics an Astronautics. All rights reserved.

引用

页码：854 / 860

页数：6

共 13 条

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