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融合动力学模型的滚动轴承性能退化评估方法

郭凯 马军 张宇 李卓睿

湖南大学学报(自然科学版)2026,Vol.53Issue(8):47-61,15.
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湖南大学学报(自然科学版)2026,Vol.53Issue(8):47-61,15.DOI:10.16339/j.cnki.hdxbzkb.2026187

融合动力学模型的滚动轴承性能退化评估方法

Rolling bearings performance degradation assessment method for integrating dynamic model

郭凯 1马军 1张宇 2李卓睿1

作者信息

  • 1. 昆明理工大学 信息工程与自动化学院,云南 昆明 650500
  • 2. 陕西中烟工业有限责任公司,陕西 西安 710065
  • 折叠

摘要

Abstract

Aiming to address the issue that single health indicators are insufficient to accurately reflect the real-time health status of rolling bearings in online scenarios,a performance degradation assessment method for rolling bearings based on a dynamic model is proposed.Built on the existing bearing dynamic model,the influence of sliding friction forces induced by speed variations on vibration response is considered,and a dynamic model capable of characterizing the health state of rolling bearings at different speeds is constructed.A model updating method based on convolutional neural networks is designed to solve the problem of online synchronous updating of dynamic models.By integrating the online synchronous dynamic model,the contact force characteristics of the dynamic model are analyzed to construct a sensitive index for characterizing the health status of the rolling bearing.Furthermore,an adaptive health status assessment strategy is established based on the dynamic correction threshold of the similarity between the measured and simulated signal.This strategy enables autonomous division of the degradation degree of the rolling bearing and dynamic update of the health index threshold.Finally,the validity of the method is verified using rolling bearing vibration experimental datasets from Xi'an Jiaotong University(XJTU-SY),the National Aeronautics and Space Administration(NASA),and a laboratory self-built platform.The experimental results demonstrate that,compared with the mainstream methods such as multivariate multi-scale fuzzy entropy and feature similarity ratio,the proposed method improves accuracy by 1.67%,reduces memory usage by 30.4%,and decreases floating-point computations by 4.55%.

关键词

滚动轴承/卷积神经网络/故障诊断/动力学模型/退化评估

Key words

rolling bearing/convolutional neural network/fault diagnosis/dynamic model/degradation assessment

分类

机械制造

引用本文复制引用

郭凯,马军,张宇,李卓睿..融合动力学模型的滚动轴承性能退化评估方法[J].湖南大学学报(自然科学版),2026,53(8):47-61,15.

基金项目

国家自然科学基金资助项目(62163020,62173168),National Natural Science Foundation of China(62163020,62173168) (62163020,62173168)

湖南大学学报(自然科学版)

1674-2974

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