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
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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.
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.
引文
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[20] 刘栋材,郭杰,申远.基于MEMD与全息谱技术的加速度传感器信息融合[J].中国科学技术大学学报,2016,46(8):69-78.LIU Dongcai,GUO Jie,SHEN Yuan.Information fusion of accelerometers based on the MEMD and holo-spectrum technology[J].Journal of University of Science and Technology of China,2016,46(8):69-78.
[21] 张维光,韩军,周翔.线结构光多分辨率测量系统数据拼接方法[J].仪器仪表学报,2013,34(7):1441-1447.ZHANG Weiguang,HAN Jun,ZHOU Xiang.Data registration method for multiresolution measurement system with line structured light[J].Chinese Journal of Scientific Instrument,2013,34(7):1441-1447.
[22] RILLING G,FLANDRIN P,GONCALVES P,et al.Bivariate empirical mode decomposition[J].IEEE Signal Process,2007,14(12):936-939.
[2] 何正嘉,曹宏瑞,訾艳阳,等.机械设备运行可靠性评估的发展与思考[J].机械工程学报,2014,50(2):171-186.HE Zhengjia,CAO Hongrui,ZI Yanyang,et al.Developments and thoughts on operational reliability assessment of mechanical equipment[J].Journal of Mechanical Engineering,2014,50(2):171-186.
[3] 李蓉,于德介,陈向民,等.基于线调频小波路径追踪算法与 EEMD 的齿轮箱复合故障诊断方法[J].振动与冲击,2014,33(3):51-56.LI Rong,YU Dejie,CHEN Xiangmin,et al.A compound fault diagnosis method for gearboxs based on chirplet path pursuit and EEMD[J].Journal of Vibration and Shock,2014,33(3):51-56.
[4] 武哲,杨绍普,张建超.基于 LMD 自适应多尺度形态学和Teager 能量算子方法在轴承故障诊断中的应用[J].振动与冲击,2016,35(3):7-13.WU Zhe,YANG Shaopu,ZHANG Jianchao.Bearing fault feature extraction method based on LMD adaptive multiscale morphology and energy operator demodulating[J].Journal of Vibration and Shock,2016,35(3):7-13.
[5] 杨宇,何知义,程军圣,等.基于 LCD-Hilbert 谱奇异值和 QRVPMCD 的滚动轴承故障诊断方法[J].振动与冲击,2015,34(7):121-126.YNAG Yu,HE Zhiyi,CHENG Junsheng,et al.Rolling bearing fault diagnosis method based on LCD-Hilbert spectrum singular values and QRVPMCD[J].Journal of Vibration and Shock,2015,34(7):121-126.
[6] 唐贵基,王晓龙.变分模态分解方法及其在滚动轴承早期故障诊断中的应用[J].振动工程学报,2016,29(4):638-649.TANG Guiji,WANG Xiaolong.Variational mode decomposition method and its application on incipient fault diagnosis of rolling bearing[J].Journal of Vibration Engineering,2016,29(4):638-649.
[7] 万海波,杨世锡.基于 HHT 的数控机床主轴振动监测系统的研制[J].振动与冲击,2014,33(6):48-52.WAN Haibo,YANG Shixi.Development of HHT-based vibration monitoring system for NC spindle[J].Journal of Vibration and Shock,2014,33(6):48-52.
[8] 郑近德,程军圣,曾鸣,等.广义经验模态分解性能分析与应用[J].振动与冲击,2015,34(3):123-128.ZHENG Jinde,CHENG Junsheng,ZENG Ming,et al.Performance analysis and application of generalized empirical mode decomposition[J].Journal of Vibration and Shock,2015,34(3):123-128.
[9] SOUTHWICK D.Using full spectrum plots[J].Orbit,1993,14:19-21.
[10] 孟建,屈梁生,刘晗.全息谱的分解及其在机械诊断中的应用[J].机械工程学报,1997,33(2):106-110.MENG Jian,QU Liangsheng,LIU Han.Holospectrum decomposition and its application in mechanical diagnosis[J].Journal of Vibration Engineering,1997,33(2):106-110.
[11] 韩捷,石来德.全矢谱技术及工程应用[M].北京:机械工业出版社,2008.
[12] 巩晓赟.基于全矢谱的非平稳故障诊断关键技术研究[D].郑州:郑州大学,2013.
[13] 黄传金,邬向伟,曹文思,等.基于 LMD 的全矢包络技术及其在 TRT 振动故障诊断中的应用[J].电力自动化设备,2015,35(2):168-174.HUANG Chuanjin,WU Xiangwei,CAO Wensi,et al.LMD-based full vector envelope technique and its application in TRT vibration fault diagnosis[J].Electric Power Automation Equipment,2015,35(2):168-174.
[14] YANG Wenxian,COURT R,TAVNER P J,et al.Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring[J].Journal of Sound and Vibration,2011,330:3766-3782.
[15] PARK C,LOONEY D,MANDIC D P,et al.The complex local mean decomposition[J].Neurocomputing,2011,74:867-875.
[16] RILLING G,FLANDRIN P,GONCALVES P,et al.Bivariate empirical mode decomposition[J].IEEE Signal Process,2007,14:936-939.
[17] REHMAN N,MANDIC D P.Empirical mode decomposition for trivariate signals[J].IEEE Transactions on Signal Processing,2010,58:1059-1068.
[18] ZHAO Xiaomin,PATEL T H,ZUO M J.Multivariate EMD and full spectrum based condition monitoring for rotating machinery[J].Mechanical Systems and Signal Processing,2012,27:712-728.
[19] 黄传金,孟雅俊,雷文平,等.复局部均值分解全矢包络技术及其在转子故障特征提取中的应用[J].机械工程学报,2016,52(7):69-78.HUANG Chuanjin,MENG Yajun,LEI Wenping,et al.Full vector envelope technique based on complex local mean decomposition and its application in fault feature extraction for rotor system[J].Journal of Mechanical of Engineering,2016,52(7):69-78.
[20] 刘栋材,郭杰,申远.基于MEMD与全息谱技术的加速度传感器信息融合[J].中国科学技术大学学报,2016,46(8):69-78.LIU Dongcai,GUO Jie,SHEN Yuan.Information fusion of accelerometers based on the MEMD and holo-spectrum technology[J].Journal of University of Science and Technology of China,2016,46(8):69-78.
[21] 张维光,韩军,周翔.线结构光多分辨率测量系统数据拼接方法[J].仪器仪表学报,2013,34(7):1441-1447.ZHANG Weiguang,HAN Jun,ZHOU Xiang.Data registration method for multiresolution measurement system with line structured light[J].Chinese Journal of Scientific Instrument,2013,34(7):1441-1447.
[22] RILLING G,FLANDRIN P,GONCALVES P,et al.Bivariate empirical mode decomposition[J].IEEE Signal Process,2007,14(12):936-939.