EMD与cICA方法在多级齿轮传动微弱故障特征提取中的应用

冷军发; 牛振华; 荆双喜; 王志阳

doi:10.13433/j.cnki.1003-8728.2017.0708

EMD与cICA方法在多级齿轮传动微弱故障特征提取中的应用

doi: 10.13433/j.cnki.1003-8728.2017.0708

1.
河南理工大学机械与动力工程学院, 河南焦作 454000

基金项目:

国家自然科学基金项目（U1304523）与河南理工大学博士基金项目（B2017-28）资助

详细信息

作者简介:
冷军发(1974-),副教授,博士研究生,研究方向为机械振动及故障诊断,lengjf@hpu.edu.cn

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- 文章访问数: 145
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- 被引次数: 0
出版历程
- 收稿日期: 2016-01-06
- 刊出日期: 2017-07-05

Weak Fault Feature Extraction of Multi-stage Gear Transmission based on EMD and Cica

1.
School of Mechanical and Power Engineering, Henan Polytechnic University, Henam Jiaozuo 454000, China

摘要

摘要: 为提取多级齿轮传动单通道测量信号中隐含的微弱低频故障特征信息，提出了一种基于经验模态分解（Empirical mode decomposition，EMD）与约束独立分量分析（Constrained independent component analysis，cICA）相结合的故障特征提取方法。首先对实测的齿轮箱单通道测量信号进行EMD分解；然后计算各个本征模态函数（Intrinsic mode function，IMF）的峭度及其与原信号的互相关系数，并选择合适的IMFs分量与原信号组成新的虚拟观测向量；最后，通过构建合适的参考信号进行cICA分析，提取出了理想的微弱低频故障特征。通过多级齿轮传动中的低速级断齿故障特征提取试验分析，验证了该方法的有效性和适用性。
- 多级齿轮传动 /
- 经验模态分解 /
- 约束独立分量分析 /
- 故障特征提取
Abstract: In order to extract the weak and low-frequency fault feature hidden in the single-channel measured signal from multi-stage gear transmissions, a joint approach of fault feature extraction based on empirical mode decomposition (EMD) and constrained independent component analysis (cICA) is proposed in this paper. Firstly, the single-channel measured signal is decomposed into several IMFs with EMD. Then, the kurtosis and cross-correlation coefficient of each IMF are computed, and the suitable IMFs for constructing the new measured virtual vector are selected. Finally, the proper reference signal including gear fault feature frequency is constructed, and the desired low-frequency slight feature is extracted with cICA method. Through the experiment analysis of fault feature extraction on the low-speed gears with a missed tooth, the effectiveness and applicability of the proposed method is verified.
- gear transmission /
- empirical mode decomposition (EMD) /
- constrained independent component analysis (cICA) /
- feature extraction

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参考文献(19)

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