基于Morlet小波变换的信号去噪及在轴承状态监测中的应用

马伦; 康建设; 赵春宇; 吕雷

doi:10.13433/j.cnki.1003-8728.2014.0913

基于Morlet小波变换的信号去噪及在轴承状态监测中的应用

doi: 10.13433/j.cnki.1003-8728.2014.0913

1.
军械工程学院装备指挥与管理系, 石家庄 050003;
2.
白城兵器试验中心, 白城 137001;
3.
62191部队, 华县 714100

详细信息

作者简介:
马伦(1985-)，博士研究生，研究方向为维修工程理论与应用和故障预测与健康管理，malun018@163.com;康建设(联系人)，教授，博士，博士生导师，zc_lcy@yahoo.cn

马伦(1985-)，博士研究生，研究方向为维修工程理论与应用和故障预测与健康管理，malun018@163.com;康建设(联系人)，教授，博士，博士生导师，zc_lcy@yahoo.cn

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出版历程
- 收稿日期: 2013-03-30

Morlet Wavelet Transform-based Signal De-noising and its Application in Bearing Condition Monitoring

1.
Dept of Equiment Command and Management, Ordnance Engineering College, Shijiazhuang 050003;
2.
Baicheng Ordnance Test Center of China, Baicheng 137001;
3.
62191 Troops of PLA, Huaxian 714100

摘要

摘要: 为在强噪声背景下利用振动信号中隐含的冲击特征成分来反映轴承性能退化趋势,提出一种基于Morlet小波变换和时域特征参数提取相结合的轴承状态监测方法。通过引入谱峭度评估Morlet小波滤波的去噪效果,再从信号滤波结果构建的组合信息中提取时域特征参数。对轴承全寿命数据的应用结果表明,特征参数的变化趋势能够监测轴承状态的劣化过程,伴随的早期故障检测可以提高轴承使用的安全性。
- Morlet小波变换 /
- 信号去噪 /
- 轴承 /
- 状态监测 /
- 时域特征参数
Abstract: In order to track degradational trend of bearing performance using shock feature hidden in the vibration signal with strong background noise,a condition monitoring method integrating Morlet wavelet transform and timedomain features extraction was proposed. The de-noising effectiveness under Morlet wavelet filtering was evaluated by spectral kurtosis. With the compounding information constructed from filtered signal,the time-domain feature parameters were extracted. The proposed method was applied to the bearing full lifetime vibration datasets,and the results show that the feature trends can reflect the degradational process of bearing condition and the bearing operational safety can be ensured by the followed caution from incipient fault detection.
- bearings(machine parts) /
- case based reasoning /
- condition monitoring /
- efficiency /
- fault detection

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参考文献(12)

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