ALIF和MCKD相结合的滚动轴承早期故障诊断

陈明; 马洁

doi:10.13433/j.cnki.1003-8728.20200182

ALIF和MCKD相结合的滚动轴承早期故障诊断

doi: 10.13433/j.cnki.1003-8728.20200182

陈明^1,,
马洁^2, ,

1.
北京信息科技大学自动化学院, 北京 100192
2.
北京信息科技大学机电工程学院, 北京 100192

基金项目:

国家自然科学基金项目 61973041

详细信息

作者简介:
陈明(1995-), 硕士研究生, 研究方向为基于数据驱动的故障诊断, 892337212@qq.com

通讯作者:
马洁, 教授, 博士生导师, 博士, mjbeijing@163.com

中图分类号: TH133.3;TP206.3
计量
- 文章访问数: 196
- HTML全文浏览量: 65
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2019-03-04
- 刊出日期: 2021-07-01

Early Fault Diagnosis of Rolling Bearings Using ALIF -MCKD

CHEN Ming^1
,,
MA Jie^{2
, ,}

1.
School of Automation, Beijing Information Science and Technology University, Beijing 100192, China
2.
School of Mechanical Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China

摘要

摘要: 滚动轴承早期故障特征信息十分微弱并夹杂着环境噪声的干扰, 使其信噪比极低, 造成微弱故障难以提取。针对这一问题, 提出了一种基于自适应局部迭代滤波(Adaptive local iterative filter, ALIF)和最大相关峭度解卷积(Maximum correlated kurtosis deconvolution, MCKD)两者相结合的滚动轴承早期故障诊断方法。首先对采集到的振动信号应用ALIF进行分解得到若干个窄带本征模态函数(Intrinsic mode functions, IMFs), 根据相关系数-峭度准则筛选出两个较为敏感的IMF分量进行重构降噪; 然后对重构降噪后的信号采用MCKD算法增强故障特征中的冲击成分; 最后对应用ALIF-MCKD增强后的信号进行包络谱解调分析, 提取出故障特征从而判断轴承故障发生位置。
- 滚动轴承 /
- 故障诊断 /
- 自适应局部迭代滤波 /
- 最大相关峭度解卷积
Abstract: Early fault feature of rolling bearings is very weak and affected by environmental noise, which makes its signal-to-noise ratio very low and makes it difficult to extract the weak fault feature. To solve this problem, this paper put forward an integrated diagnosis method based on the adaptive local iterative filter (ALIF) and maximum correlated kurtosis deconvolution (MCKD). Firstly the collected vibration signals were decomposed by ALIF into a number of narrow band intrinsic mode functions (IMFs), and two sensitive IMF components screened out according to the correlation coefficient -kurtosis criterion are reconstructed for noise reduction. Then, the MCKD algorithm is used to enhance the shock component of the fault characteristics. Finally, the envelope spectrum demodulation analysis was carried out for the signal enhanced by the application of ALIF-MCKD and the fault characteristics were extracted to determine the bearing fault location.
- rolling bearing /
- fault diagnosis /
- adaptive local iterative filtering /
- maximum correlation kurtosis deconvolution

HTML全文

图 1 MCKD算法流程图

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图 2 ALIF-MCKD滚动轴承故障诊断流程

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图 3 无噪声干扰预期时域波形及频谱图

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图 4 无噪声干扰应用EMD算法分解结果

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图 5 无噪声干扰下应用ALIF算法分解结果

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图 6 含噪声干扰应用下ALIF算法分解结果

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图 7 仿真信号时域波形

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图 8 仿真信号包络谱

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图 9 仿真信号ALIF分解

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图 10 仿真信号ALIF-MCKD包络谱

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图 11 实验装置示意图

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图 12 外圈故障信号时域波形

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图 13 外圈故障信号频谱

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图 14 ALIF分解结果

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图 15 ALIF重构信号包络谱

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图 16 ALIF-MCKD增强后外圈故障时域波形

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图 17 ALIF-MCKD增强后外圈故障包络谱

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表 1 6205-2RS轴承的基本参数

滚动体径d/mm	滚动体数m/个	接触角α/(°)	节圆直径D/mm
7.94	9	0	39.04

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表 2 ALIF分解后各分量峭度和相关系数

IMF分量	IMF₁	IMF₂	IMF₃	IMF₄
峭度值	3.653 0	4.735 0	5.254 3	6.859 0
相关系数	0.946 1	0.925 5	0.861 1	0.765 7

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