矿用带式输送机驱动滚筒轴承振动信号降噪方法
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摘要
针对现有振动信号降噪方法中经验模态分解存在模态混叠、独立分量分析要求采集的振动信号数不少于源信号数等问题,提出了一种基于集合经验模态分解(EEMD)和快速独立分量分析(FastICA)的矿用带式输送机驱动滚筒轴承振动信号降噪方法。首先,通过EEMD算法对采集的振动信号进行分解,得到若干不同尺度的包含故障特征频率的本征模态函数(IMF)分量;然后,基于相关系数对IMF分量进行重构,得到特征信号和虚拟噪声信号,将重构的特征信号和虚拟噪声信号组成输入矩阵,并作为FastICA算法的输入;最后,利用FastICA算法实现信号与噪声分离,达到信号降噪的目的。实验结果验证了该方法的可行性和有效性。
EMD has modal aliasing and ICA requires the number of collected vibration signal should not be less than the number of source signal.In view of the above problems that existed in existing vibration signal denoising method,a vibration signal denoising method for drive roller bearing of mine-used belt conveyor was proposed which was based on EEMD and FastICA.Firstly,collected vibration signal is decomposed by EEMD algorithm,so as to obtain several IMF components at different scales that containing faults characteristic frequency.Then,the IMF components are reconstructed based on correlation coefficient to obtain characteristic signal and virtual noise signal,which are formed into input matrix as input of FastICA algorithm.Finally,FastICA algorithm is used to separate signal and noise to achieve purpose of signal denoising.The experimental results verify feasibility and effectiveness of the method.
EMD has modal aliasing and ICA requires the number of collected vibration signal should not be less than the number of source signal.In view of the above problems that existed in existing vibration signal denoising method,a vibration signal denoising method for drive roller bearing of mine-used belt conveyor was proposed which was based on EEMD and FastICA.Firstly,collected vibration signal is decomposed by EEMD algorithm,so as to obtain several IMF components at different scales that containing faults characteristic frequency.Then,the IMF components are reconstructed based on correlation coefficient to obtain characteristic signal and virtual noise signal,which are formed into input matrix as input of FastICA algorithm.Finally,FastICA algorithm is used to separate signal and noise to achieve purpose of signal denoising.The experimental results verify feasibility and effectiveness of the method.
引文
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[19]靳行,林建辉,伍川辉,等.基于EEMD-TEO熵的高速列车轴承故障诊断方法[J].西南交通大学学报,2018,53(2):359-366.JIN Hang,LIN Jianhui,WU Chuanhui,et al.Diagnostic method for high-speed train bearing fault based on EEMD-TEO entropy[J].Journal of Southwest Jiaotong University,2018,53(2):359-366.
[2]TIAN Pengfei,ZHANG Lei,CAO Xianjie,et al.The application of EMD-CIIT lidar signal denoising method in aerosol detection[J].Procedia Engineering,2015,102(4):1233-1237.
[3]刘东瀛,邓艾东,刘振元,等.基于EMD与相关系数原理的故障声发射信号降噪研究[J].振动与冲击,2017,36(19):71-77.LIU Dongying,DENG Aidong,LIU Zhenyuan,et al.De-noising method for fault acoustic emission signals based on the EMD and correlation coefficient[J].Journal of Vibration and Shock,2017,36(19):71-77.
[4]LI Xiaoyu,JIN Jing,SHEN Yi,et al.Noise level estimation method with application to EMD-based signal denoising[J].Journal of Systems Engineering&Electronics,2016,27(4):763-771.
[5]FENGRONG B,TENG M,XU W.Development of a novel knock characteristic detection method for gasoline engines based on wavelet-denoising and EMDdecomposition[J].Mechanical Systems and Signal Processing,2019,117:517-536.
[6]包广清,常勇,杨国金.基于EMD阈值方法的轴承故障振动信号去噪[J].计算机工程与应用,2015,51(10):205-210.BAO Guangqing,CHANG Yong,YANG Guojin.Denoising of rolling bearing fault vibration signal based on empirical mode decomposition threshold[J].Computer Engineering and Applications,2015,51(10):205-210.
[7]贾瑞生,赵同彬,孙红梅,等.基于经验模态分解及独立成分分析的微震信号降噪方法[J].地球物理学报,2015,58(3):1013-1023.JIA Ruisheng,ZHAO Tongbin,SUN Hongmei,et al.Micro-seismic signal denoising method based on empirical mode decomposition and independent component analysis[J].Chinese Journal of Geophysics,2015,58(3):1013-1023.
[8]季忠,金涛,杨炯明,等.基于独立分量分析的消噪方法在旋转机械特征提取中的应用[J].中国机械工程,2005,16(1):50-53.JI Zhong,JIN Tao,YANG Jiongming,et al.Applications of noise-reduction with ICA in feature extraction of rotating machinery[J].China Mechanical Engineering,2005,16(1):50-53.
[9]LEI Yaguo,HE Zhengjia,ZI Yanyang.Application of the EEMD method to rotor fault diagnosis of rotating machinery[J].Mechanical Systems&Signal Processing,2009,23(4):1327-1338.
[10]WU Zhaohua,HUANG N E.Ensemble empirical mode decomposition:a noise assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[11]钱沛云,陈曦晖,胡晓,等.基于振动信号分析的采煤机摇臂轴承故障诊断研究[J].煤炭科学技术,2014,42(12):89-92.QIAN Peiyun,CHEN Xihui,HU Xiao,et al.Study on bearing fault diagnosis of ranging boom in coal shearer based on vibration signal analysis[J].Coal Science and Technology,2014,42(12):89-92.
[12]王增才,王树梁,任锴胜,等.基于EEMD的提升机天轮轴承故障诊断方法[J].煤炭学报,2012,37(4):689-694.WANG Zengcai,WANG Shuliang,REN Kaisheng,et al.Research on the method of hoist head sheave bearing fault diagnosis based on EEMD[J].Journal of China Coal Society,2012,37(4):689-694.
[13]郝尚清,庞新宇,王雪松,等.基于盲源分离的采煤机摇臂轴承故障诊断方法[J].煤炭学报,2015,40(11):2509-2513.HAO Shangqing,PANG Xinyu,WANG Xuesong,et al.Bearing fault diagnosis method for shearer rocker arm based on blind source separation[J].Journal of China Coal Society,2015,40(11):2509-2513.
[14]易长平,赵明生,崔正荣.基于独立分量分析的爆破振动信号分离仿真试验[J].爆破,2010,27(1):31-32.YI Changping,ZHAO Mingsheng,CUI Zhengrong.Simulation experiment of separation of blasting vibration signal based on ICA method[J].Blasting,2010,27(1):31-32.
[15]叶红仙,杨世锡,杨将新.多振源卷积混合的时域盲源分离算法[J].机械工程学报,2009,45(1):189-194.YE Hongxian,YANG Shixi,YANG Jiangxin.Temporal blind source separation algorithm for convolution mixtures with muti vibration sources[J].Journal of Mechanical Engineering,2009,45(1):189-194.
[16]LINH-TRUNG N,BELOUCHRANI A,ABED-MERAIM K,et al.Separating more sources than sensors using time-frequency distributions[J].Journal on Advances in Signal Processing,2005,2005(17):1-20.
[17]李云霞.盲信号分离算法及其应用[D].成都:电子科技大学,2008.
[18]毋文峰,陈小虎,苏勋家.基于经验模式分解的单通道机械信号盲分离[J].机械工程学报,2011,47(4):12-16.WU Wenfeng,CHEN Xiaohu,SU Xunjia.Blind source separation of single-channel mechanical signal based on empirical mode decomposition[J].Journal of Mechanical Engineering,2011,47(4):12-16.
[19]靳行,林建辉,伍川辉,等.基于EEMD-TEO熵的高速列车轴承故障诊断方法[J].西南交通大学学报,2018,53(2):359-366.JIN Hang,LIN Jianhui,WU Chuanhui,et al.Diagnostic method for high-speed train bearing fault based on EEMD-TEO entropy[J].Journal of Southwest Jiaotong University,2018,53(2):359-366.