| 引用本文: | 黄传金,宋海军,秦娜,陈晓,柴鹏.基于复局部均值分解和复信号包络谱的滚动轴承故障诊断方法[J].电力自动化设备,2020,40(8): |
| HUANG Chuanjin,SONG Haijun,QIN Na,CHEN Xiao,CHAI Peng.Fault diagnosis method of rolling bearing based on CLMD and CSES[J].Electric Power Automation Equipment,2020,40(8): |
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| 摘要: |
| 提出了一种基于复局部均值分解(CLMD)和复信号包络谱(CSES)的滚动轴承故障诊断新方法。首先通过互相垂直安装的加速度传感器采集2个方向的振动信号，并将其组成一个复数信号；然后利用CLMD对二元复数信号进行自适应分解，将分解得到的复数信号的实部和虚部包络信号组成一个复包络信号，根据复傅里叶变换具有幅值增强和综合频率特性，直接对复包络信号进行复傅里叶变换，提取的故障特征频率更为清晰。通过滚动轴承不同位置的外圈故障实验，证明了所提方法能够实现故障特征增强，可用于诊断滚动轴承微弱故障和复合故障。 |
| 关键词: 滚动轴承 早期故障 故障诊断 复局部均值分解 复傅里叶变换 复信号包络谱 |
| DOI：10.16081/j.epae.202006016 |
| 分类号:TK286.1 |
| 基金项目:国家自然科学基金资助项目(61433011,61603316)；河南省创新型科技人才队伍建设工程资助项目(C20150034)；河南省高等学校重点科研项目(19A460029)；河南科技攻关项目(202102210077)；郑州工程技术学院科技创新团队建设项目(CXTD2017K1) |
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| Fault diagnosis method of rolling bearing based on CLMD and CSES |
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HUANG Chuanjin1, SONG Haijun1, QIN Na2, CHEN Xiao1, CHAI Peng1
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1.School of Mechanical and Electrical Engineering, Zhengzhou Institute of Technology, Zhengzhou 450044, China;2.School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
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| Abstract: |
| A novel fault diagnosis method of rolling bearing based on CLMD(Complex Local Mean Decomposition) and CSES(Complex Signal Envelope Spectrum) is proposed. Firstly, the vibration signals in two directions are collected by the acceleration sensors installed perpendicularly to each other and combined into a complex signal. Then the complex signals are adaptively decomposed by CLMD, and the real and imaginary envelope signals obtained from the complex signal are combined into a complex envelope signal. According to the amplitude enhancement and composite frequency feature of complex Fourier transform, the complex Fourier transform is directly applied to the complex envelope signal, and the extracted fault characteristic frequency is clearer. By the outer ring fault experiment in different position of rolling bearing, it is proved that the proposed method can enhance the fault feature and can be used to diagnose weak faults and compound faults of rolling bearing. |
| Key words: rolling bearing early failure fault diagnosis complex local mean decomposition complex Fourier transform complex signal envelope spectrum |
