[1]张雨琦,邹金慧,马军.多退化变量灰色预测模型的滚动轴承剩余寿命预测[J].ag亚游ag8|官方,2019,41(03):112.[doi:.]
 ZHANG Yuqi,ZOU Jinhui,MA Jun.Rolling Bearing Residual Life Prediction Based on Grey Prediction Model with Multiple Degenerate Variables[J].,2019,41(03):112.[doi:.]
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多退化变量灰色预测模型的滚动轴承剩余寿命预测()
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《ag亚游ag8|官方》[ISSN:1008-1194/CN:61-1316/TJ]

卷:
41
期数:
2019年03
页码:
112
栏目:
出版日期:
2019-06-24

文章信息/Info

Title:
Rolling Bearing Residual Life Prediction Based on Grey Prediction Model with Multiple Degenerate Variables
文章编号:
1008-1194(2019)03-0112-09
作者:
张雨琦1;?2;?邹金慧1;?2;?马军3
1.昆明理工大学信息工程与自动化学院,云南 昆明 650500;2.云南省矿物管道输送工程技术研究中心,云南 昆明 650500;3.昆明理工大学机电工程学院,云南 昆明 650500
Author(s):
ZHANG Yuqi1;?2;?ZOU Jinhui1;?2;?MA Jun3
1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; 2. Engineering Research Center for Mineral Pipeline Transportation of Yunnan Province, Kunming 650500, China; 3. Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
关键词:
剩余寿命预测;?滚动轴承;?多退化变量灰色预测模型;?退化趋势特征参数
Keywords:
residual life prediction;? rolling bearing;? grey prediction model with multiple degenerate variables;? characteristic parameter of degenerate trend
分类号:
TH17
DOI:
.
文献标志码:
A
摘要:
针对单一退化变量预测滚动轴承性能退化趋势时可靠性和误差精度较低的问题,提出了基于多退化变量灰色预测模型的滚动轴承剩余寿命预测方法。该方法通过提取滚动轴承全寿命周期振动信号的退化趋势特征参数集,结合退化趋势特征参数集及故障早期突变点,实现滚动轴承的早期故障识别;并根据轴承寿命与特征参数之间的映射关系建立多退化变量灰色预测模型对轴承的剩余寿命进行预测。仿真实验结果表明,多退化变量灰色预测模型具有更高的误差精度和可靠性,其预测效果优于BP神经网络、单一退化变量灰色预测以及SVR(支持向量回归)预测模型,能够更好对滚动轴承寿命的变化趋势进行表征。
Abstract:
Aiming at the low reliability and error accuracy in predicting the degradation trend in rolling bearing when using single degenerate variable, a novel method based on grey prediction model with multiple degenerate variables was proposed to predict the residual life of rolling bearings. First of all, the characteristic parameter set related to degradation vibration signals of lifetime bearing was extracted. Then, the early fault identification of rolling bearing was completed by integrating the degradation trend characteristic parameter set and early breakdown point. And the grey prediction model with multiple degenerate variables was established through the mapping relationship between bearing life and characteristic parameters to predict the residual life of the rolling bearing. The simulation results showed that the grey prediction model with multiple degradation variables had higher error accuracy and reliability when compared with the grey prediction model with single degenerate variable, the prediction model of BP neural network and the prediction model of SVR (Support Vector Regression). It could characterize the degradation trend of rolling bearing life better.

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备注/Memo

备注/Memo:
收稿日期:2019-01-03
基金项目:国家自然科学基金资助项目资助(61663017&61563024&61741310)
作者简介:张雨琦(1993—),女,湖北襄阳人,硕士研究生,研究方向:机械设备故障诊断及寿命预测。E-mail:18833010@qq.com。
更新日期/Last Update: 2019-07-11