[1]夏 平,高龙飞,张立金,等.强噪声背景下的 GWO-VMD 滚动轴承故障诊断方法[J].机械与电子,2025,(11):68-73.
　XIA Ping,GAO Longfei,ZHANG Lijin,et al.GWO-VMD Method for Rolling Bearing Fault Diagnosis under Strong Noise Conditions[J].Machinery & Electronics,2025,(11):68-73.
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强噪声背景下的 GWO-VMD 滚动轴承故障诊断方法()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年11期
页码:
68-73
栏目:
机电一体化
出版日期:
2025-11-24

文章信息/Info

Title:
GWO-VMD Method for Rolling Bearing Fault Diagnosis under Strong Noise Conditions
文章编号:
1001-2257 ( 2025 ) 11-0068-06
作者:
夏 平 1 ; 高龙飞 1 ; 张立金 1 ; 雷默涵 2
1. 西安石油大学机械工程学院,陕西 西安 710065 ;
2. 西安理工大学机械与精密仪器工程学院,陕西 西安 710048
Author(s):
XIA Ping1 ;  GAO Longfei1 ;  ZHANG Lijin1 ;  LEI Mohan2
( 1.School of Mechanical Engineering , Xi ’ an Shiyou University , Xi ’ an 710065 , China ;
2.School of Mechanical and Precision Instrument Engineering , Xi ’ an University of Technology , Xi ’ an 710048 , China )
关键词:
滚动轴承; 故障诊断; 灰狼优化算法; 变分模态分解; 能量熵与峭度比
Keywords:
rolling bearing ;  fault diagnosis ;  grey wolf optimizer ( GWO );  variational mode decomposition ( VMD );  the ratio of energy entropy to kurtosis
分类号:
TH133.33 ;TP18
文献标志码:
A
摘要:
为有效提取强噪声背景下的滚动轴承故障特征,提出一种基于灰狼优化算法( GWO )的变分模态分解( VMD )方法。首先,为解决 VMD 的参数选取问题,构建能量熵与峭度的比值(?RHK?)作为目标函数,通过 GWO 确定 VMD 的最优参数组合;其次,为解决 VMD 主模态分量难以选取问题,通过计算所分解模态分量的 RHK 值选取主模态分量;最后,通过包络谱分析实现故障诊断。经仿真信号分析、公开数据集验证以及实验信号测试,验证了所提方法在强噪声环境中能够有效地完成滚动轴承的故障诊断。与传统包络解调法和单一峭度指标筛选的有效分量的包络谱相比,所提方法具有更好的鲁棒性。
Abstract:
To effectively extract rolling bearing fault characteristics under strong noise conditions , a method based on the Grey Wolf Optimizer ( GWO ) and Variational Mode Decomposition ( VMD ) was proposed.First , to address the parameter selection issue of VMD , the ratio of energy entropy to kurtosis ( RHK ) was constructed as the objective function.The optimal parameter combination of VMD was determined using GWO.Second , to tackle the difficulty in selecting main mode components of VMD , RHK values of the decomposed mode components were calculated to select the main mode components.Finally , fault diagnosis was achieved through envelope spectrum analysis.The proposed method is validated to effectively perform rolling bearing fault diagnosis under strong noise through simulated signal analysis , public dataset validation , and experimental signal testing.Compared with the traditional envelope demodulation method and the envelope spectrum using a single kurtosis index for component selection , the proposed method exhibits greater robustness.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2025-05-26
基金项目:陕西省自然科学基金青年资助项目( 2023-JC-QN-0505 )
作者简介:夏 平 ( 1986- ),女,吉林长春人,博士,实验师,硕士研究生导师,研究方向为机械设备故障诊断与状态监测、机械信号处理及机械部件故障机理;雷默涵 ( 1987- ),男,甘肃武威人,博士,讲师,硕士研究生导师,研究方向为精密机床及其部件运行模态特性及热误差分析等,通信作者, E-mail : salingerr@outlook.com 。
更新日期/Last Update: 2025-12-15