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旋转机械故障特征提取的全矢二元经验模态分解方法研究 被引量:7

Full vector BEMD method for fault feature extraction of rotating machinery
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摘要 为更准确提取旋转机械故障特征,提出了基于全矢二元经验模态分解(Bivariate Empirical Mode Decomposition,BEMD)的故障特征提取方法。该方法首先通过多传感器正交采集旋转机械故障同一截面上的振动信号,并将其组成一个复数;然后运用BEMD将复数按旋转速度从高到低的顺序自适应地分解到各自的频带,得到系列复固有模态分量(Complex Intrinsic Mode Functions, CIMFs);提出复数相关系数的概念,并用于组合CIMFs得到新的复旋转分量以防同一频率的信号被分解到不同的CIMFs;最后,运用全矢谱融合组合后的CIMFs的特征信息,得到幅频、角度和进动方向等信息。与全频谱方法的对比试验结果表明该方法的有效性。 A method of full vector bivariate empirical mode decomposition (BEMD) was proposed to more accurately extract fault characteristics of rotating machinery. Firstly, vibration signals at fault position’s cross-section of rotating machinery were collected with orthogonally located multi-sensor to form a complex. Then, BEMD method was applied to adaptively decompose the complex into different frequency bands according to rotating speed values’ high to low turn to obtain complex intrinsic mode functions (CIMFs). Finally, the full vector spectrum technique was used to fuse characteristic information of CIMFs to acquire information of amplitude-frequency, phase-frequency and precession directions. The test results using the proposed method were compared with those using the full frequency spectrum one. It was shown that the proposed method is effective.
作者 黄传金 雷文平 李凌均 孟雅俊 赵静 HUANG Chuanjin;LEI Wenping;LI Lingjun;MENG Yajun;ZHAO Jing(School of Mechanical and Electrical Vehicle Engineering, Zhengzhou Institute of Technology, Zhengzhou 450044, China;School of Mechanical Engineering, Zhejiang University, Hangzhou 310017, China;School of Mechanical Engineering, Zhengzhou University, Zhengzhou 450052, China)
出处 《振动与冲击》 EI CSCD 北大核心 2019年第9期94-99,132,共7页 Journal of Vibration and Shock
基金 河南省创新型科技人才队伍建设工程(C20150034) 河南省科技攻关项目科技攻关项目(172102210116) 河南省高等学校重点科研项目(18A460006 19A460029) 郑州工程技术学院科技创新团队建设计划资助项目(CXTD2017K1)
关键词 旋转机械 特征提取 二元经验模态分解 全矢谱 rotating machinery fault feature extraction bivariate empirical mode decomposition (BEMD) full vector spectrum
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