NSMD和LMSST相结合的变转速滚动轴承故障诊断方法

尤光辉; 吕勇; 易灿灿; 余肇鸿

doi:10.13433/j.cnki.1003-8728.20220223

NSMD和LMSST相结合的变转速滚动轴承故障诊断方法

doi: 10.13433/j.cnki.1003-8728.20220223

尤光辉^{1, 2, 3,},
吕勇^{1, 2, ,},
易灿灿^{1, 2},
余肇鸿^{1, 2}

1.
武汉科技大学冶金装备及其控制教育部重点实验室, 武汉 430081
2.
武汉科技大学机械传动与制造工程湖北省重点实验室, 武汉 430081
3.
浙江机电职业技术学院, 杭州 310053

基金项目:

国家自然科学基金面上项目 51875416

国家自然科学基金项目 51805382

湖北省自然科学基金创新群体项目 2020CFA033

浙江省教育厅一般科研项目 Y202148122

详细信息

作者简介:
尤光辉(1989-), 讲师, 博士研究生, 研究方向为信号处理及机械故障诊断, youguanghui@zime.zj.cn

通讯作者:
吕勇, 教授, 博士生导师, lvyong@wust.edu.cn

中图分类号: TH133.33
计量
- 文章访问数: 141
- HTML全文浏览量: 183
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2021-12-22
- 刊出日期: 2022-10-25

Fault Diagnosis Method of Variable Speed Rolling Bearings Combined with NSMD and LMSST

YOU Guanghui^{1, 2, 3
,},
LYU Yong^{1, 2
, ,},
YI Cancan^{1, 2},
YU Zhaohong^{1, 2}

1.
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
2.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
3.
Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China

摘要

摘要: 为了能够准确反映变转速工况下滚动轴承的时变故障特征，本文提出了一种基于非线性稀疏模态分解(NSMD)和局部最大值同步压缩变换(LMSST)的故障诊断方法。首先利用NSMD对含噪振动信号进行分解，基于各分量的频谱最大相关性进行有用分量的选择；然后对其进行LMSST分析，从时频平面中提取时变故障特征，从而实现变转速下轴承故障诊断。
- 非线性稀疏模态分解 /
- 局部最大值同步压缩变换 /
- 滚动轴承 /
- 故障诊断
Abstract: In order to accurately reflect the time-varying fault characteristics of rolling bearings under variable speed conditions, a new fault diagnosis method based on Nonlinear Sparse Mode Decomposition (NSMD) and Local Maximum Synchrosqueezing Transform (LMSST) is proposed in this paper. Firstly, the noisy vibration signal of rolling bearings is decomposed by NSMD, and the useful components are selected based on the maximum spectral correlation of each component; Then these useful components are treated again though LMSST analysis, and the time-varying fault features are extracted from the time-frequency plane, so as to realize the bearing fault diagnosis under variable speed conditions.
- nonlinear sparse mode decomposition /
- local maximum synchrosqueezing transform /
- rolling bearings /
- fault diagnosis

HTML全文

图 1 本文提出方法的流程图

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图 2 仿真信号时域图及频谱

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图 3 仿真信号理想IF曲线

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图 4 LMD、VMD和NSMD这3种算法对仿真信号的分解结果及其频谱

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图 5 4种时频分析算法的结果

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图 6 试验台实物图及结构简图

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图 7 振动信号时域图及频谱

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图 8 NSMD对实测振动信号的分解结果及频谱

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图 9 4种时频分析算法对分量1的计算结果

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图 10 LMSST结果脊线提取

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图 11 计算故障特征频率与理论值比较

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表 1 仿真模拟信号不同分析方法得到的Renyi熵值比较

时频分析方法 SST MSST SST2 LMSST

Renyi熵值 15.264 8 14.521 2 13.856 8 10.268 4

下载: 导出CSV

表 2 实际信号不同分析方法得到的Renyi熵值比较

时频分析方法 SST MSST SST2 LMSST

Renyi熵值 16.241 8 17.066 1 16.127 6 14.833 5

下载: 导出CSV

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