自适应噪声极值加权模态分解及其在低速滚动轴承故障诊断中的应用

苏缪涎; 郑近德; 潘海洋; 童靳于; 刘庆运; 潘紫微

doi:10.13433/j.cnki.1003-8728.20190329

自适应噪声极值加权模态分解及其在低速滚动轴承故障诊断中的应用

doi: 10.13433/j.cnki.1003-8728.20190329

苏缪涎^1,,
郑近德^{1, 2, ,},
潘海洋¹,
童靳于¹,
刘庆运¹,
潘紫微¹

1.
安徽工业大学机械工程学院, 安徽马鞍山 243032
2.
安徽理工大学矿山智能装备与技术安徽省重点实验室, 安徽淮南 232001

基金项目:

国家重点研发计划项目 2017YFC0805100

国家自然科学基金项目 51975004

国家自然科学基金项目 51505002

安徽省高校自然科学研究重点项目 KJ2019A053

安徽省高校自然科学研究重点项目 KJ2019A092

详细信息

作者简介:
苏缪涎(1994-), 硕士研究生, 研究方向为机械故障诊断, mxsu1994@126.com

通讯作者:
郑近德, 副教授, 硕士生导师, 博士, lqdlzheng@126.com

中图分类号: TH17
计量
- 文章访问数: 207
- HTML全文浏览量: 51
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2019-06-24
- 刊出日期: 2020-11-01

Complete Ensemble Extreme-point Weighted Mode Decomposition with Adaptive Noise and its Application to Fault Diagnosis of Low Speed Rolling Bearings

1.
School of Mechanical Engineering, Anhui University of Technology, Anhui Ma'anshan 243032, China
2.
Anhui Key Laboratory of Mine Intelligent Equipment and Technology, Anhui University of Science&Technology, Anhui Huainan 232001, China

摘要

摘要: 极值加权模态分解（Extreme-point weighted mode decomposition，EWMD）是在经验模态分解（Empirical mode decomposition，EMD）的基础上提出的一种自适应信号分解方法，能够改善EMD的分解能力。针对于EWMD的模态混叠问题，借鉴噪声辅助分解思想，提出了自适应噪声极值加权模态分解（Complete ensemble extreme-point weighted mode decomposition with adaptive noise，CEEWMDAN）。CEEWMDAN通过向原始信号中添加辅助噪声，用改进的均值曲线构造方式提取内禀模态函数，有效地抑制模态混叠，且分解结果对集成次数的依赖性小，在保证分解精度的前提下减少了计算量。通过仿真实验数据分析验证了CEEWMDAN在提高分解性能和抑制模态混叠方面的有效性。提出了一种基于CEEWMDAN和快速谱峭度的低速滚动轴承故障诊断方法，并应用于实测数据分析。结果表明所提出的方法能够有效地识别低速滚动轴承故障。
- EMD /
- EWMD /
- 模态混叠 /
- 滚动轴承 /
- 故障诊断 /
- 低速
Abstract: Extreme-point weighted mode decomposition (EWMD) is an adaptive signal processing method based on empirical mode decomposition (EMD). Aiming at mode mixing problem of EWMD, the complete ensemble extreme-point weighted mode decomposition with adaptive noise (CEEWMDAN) is proposed in this paper. CEEWMDAN extracts intrinsic mode function by adding the assisted noise to original signal and using the improved mean curve, which effectively restrains the mode mixing and reduces the computational complexity. Its effectiveness is verified by simulation experimental data analysis. Finally, a fault diagnosis method for low-speed rolling bearings based on CEEWMDAN and fast spectral kurtosis is proposed and applied to the measured data analysis. The results show that the proposed fault diagnosis method can effectively identify low-speed rolling bearing faults.
- EMD /
- EWMD /
- mode mixing /
- rolling bearing /
- fault diagnosis /
- low speed

HTML全文

图 1 不同α分解结果的正交性指标和能量比

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图 2 不同集成次数的分解结果的正交性指标和能量比

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图 3 仿真信号的时域图以及EWMD、CEEMDAN和CEEWMDAN方法的分解结果

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图 4 ID-25/30型轴承全寿命试验台的结构示意图

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图 5 内圈故障轴承

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图 6 轴承内圈故障信号时域图

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图 7 CEEWMDAN的分解结果和包络功率谱

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图 8 EWMD的分解结果和包络功率谱

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图 9 CEEMDAN的分解结果和包络功率谱

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表 1 IMF的能量比ER和正交性指标IO

指标	CEEMDAN	CEEWMDAN	EWMD
ER₁	0.449 5	0.386 8	10.098 4
ER₂	0.549 2	0.031 3	1.185 1
ER₃	0.023 2	0.032 1	0.561 5
IO	0.144 9	0.094 2	44.759 5

下载: 导出CSV

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