改进的噪声总体集合经验模式分解方法在轴承故障诊断中的应用
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摘要
在复杂的流程工业中,机械设备往往处在高速、重载、高温、高辐射的环境中,轴承作为主要的机械零部件起着重要作用。由于轴承故障振动信号的微弱和不平稳的特性,造成故障特征向量提取和故障诊断存在着困难。提出一种改进的CEEMDAN(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,ICEEMDAN)轴承故障诊断方法。通过对比分析仿真信号和实测信号可以得知:ICEEMDAN方法可以改善信号重构质量,具有良好的自适应性,能够提高故障信号的信噪比,从而可以有效地识别并提取有用的故障特征信息。
In the complex process of industry,the machinery equipment is often working in rigorous conditions such as highspeed,heavy load,high temperature and high radiation. Thus,the rolling bearing plays an important role in the maintaining mechanical components. To date,it is difficulty to extract the fault feature due to that the characteristics about the fault vibration signal is weak and unstable. The presents an improved CEEMDAN(ICEEMDAN)bearing fault diagnosis method.Researching by the simulation signal and the measured signal analysis,the proposed method can effectively improve the quality of reconstructed signals and have a good self-adaptability to enhance signal-to-noise of the fault signal. Therefore,it can identify and extract useful fault characteristic information effectively.
In the complex process of industry,the machinery equipment is often working in rigorous conditions such as highspeed,heavy load,high temperature and high radiation. Thus,the rolling bearing plays an important role in the maintaining mechanical components. To date,it is difficulty to extract the fault feature due to that the characteristics about the fault vibration signal is weak and unstable. The presents an improved CEEMDAN(ICEEMDAN)bearing fault diagnosis method.Researching by the simulation signal and the measured signal analysis,the proposed method can effectively improve the quality of reconstructed signals and have a good self-adaptability to enhance signal-to-noise of the fault signal. Therefore,it can identify and extract useful fault characteristic information effectively.
引文
[1]Huang N E,Shen Z,Long S R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]//Proceedings of the Royal Society of London A:Mathematical,Physical and Engineering Sciences.The Royal Society,1998,454(1971):903-995.
[2]顾有林,叶应流,曹光华.EMD和小波变换在低可探测目标检测中的应用[J].红外与激光工程,2015,44(11):3494-3499.(Gu You-lin,Ye Ying-liu,Cao Guang-hua.Application of EMD and wavelet transform in low detectable targets detection[J].Infrared and Laser Engineering,2015,44(11):3494-3499.)
[3]胡爱军,孙敬敬,向玲.经验模态分解中的模态混叠问题[J].振动.测试与诊断,2011,31(4):429-434.(Hu Ai-jun,Sun Jing-jing,Xiang Ling.Modal aliasing problem in empirical mode decomposition[J].Journal of Vibration Measurement&Diagnosis,2011,31(4):429-434.)
[4]Torres M E,Colominas M A,Schlotthauer G.A complete ensemble empirical mode decomposition with adaptive noise[C]//2011 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2011:4144-4147.
[5]Yeh J R,Shieh J S,Huang N E.Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010(2):135-156.
[6]Humeau-Heurtier A,Abraham P,MahéG.Analysis of laser speckle contrast images variability using a novel empirical mode decomposition:comparison of results with laser doppler flowmetry signals variability[J].IEEE transactions on medical imaging,2015,34(2):618-627.
[7]Colominas M A,Schlotthauer G,Torres M E.Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J].Biomedical Signal Processing and Control,2014(14):19-29.
[8]卢珍.关于经验模态分解与整体经验模态分解的分离效果差别的探讨[J].科学技术与工程,2011,11(33):8353-8356.(Lu Zhen.A Study on the Different Separation Effect between EMD and EEMD[J].Science Technology and Engineering,2011,11(33):8353-8356.)
[9]刘琦.基于小波分析的轴承故障诊断研究[J].煤,2013,22(7):12-14.(Liu Qi.Research on bearing fault diagnosis based on wavelet[J].Coal,2013,22(7):12-14.)
[10]陈仁祥,汤宝平,吕中亮.基于相关系数的EEMD转子振动信号降噪方法[J].振动.测试与诊断,2012,32(4):542-546.(Chen Ren-xiang,Tang Bao-ping,Lv Zhong-liang.EEMD rotor vibration signal denoising method based on correlation coefficient[J].Journal of Vibration Measurement&Diagnosis,2012,32(4):542-546.)
[11]郭福平,沈书乾,段志宏.基于包络谱分析的滚动轴承内圈故障声发射诊断研究[J].机床与液压,2015,43(17):210-212.(Guo Fu-ping,Shen Shu-qian,Duan Zhi-hong.Study of acoustic emission diagnosis of rolling bearing inner ring fault based on envelope spectrum analysis[J].Machine Tool&Hydraulics,2015,43(17):210-212.)
[12]廖伯瑜.机械故障诊断基础[J].北京:冶金工业出版社,1995(995).(Liao Bai-yu.Mechanical fault diagnosis foundation[J].Beijing:Metallurgical Industry Press,1995(995).)
[2]顾有林,叶应流,曹光华.EMD和小波变换在低可探测目标检测中的应用[J].红外与激光工程,2015,44(11):3494-3499.(Gu You-lin,Ye Ying-liu,Cao Guang-hua.Application of EMD and wavelet transform in low detectable targets detection[J].Infrared and Laser Engineering,2015,44(11):3494-3499.)
[3]胡爱军,孙敬敬,向玲.经验模态分解中的模态混叠问题[J].振动.测试与诊断,2011,31(4):429-434.(Hu Ai-jun,Sun Jing-jing,Xiang Ling.Modal aliasing problem in empirical mode decomposition[J].Journal of Vibration Measurement&Diagnosis,2011,31(4):429-434.)
[4]Torres M E,Colominas M A,Schlotthauer G.A complete ensemble empirical mode decomposition with adaptive noise[C]//2011 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2011:4144-4147.
[5]Yeh J R,Shieh J S,Huang N E.Complementary ensemble empirical mode decomposition:A novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010(2):135-156.
[6]Humeau-Heurtier A,Abraham P,MahéG.Analysis of laser speckle contrast images variability using a novel empirical mode decomposition:comparison of results with laser doppler flowmetry signals variability[J].IEEE transactions on medical imaging,2015,34(2):618-627.
[7]Colominas M A,Schlotthauer G,Torres M E.Improved complete ensemble EMD:A suitable tool for biomedical signal processing[J].Biomedical Signal Processing and Control,2014(14):19-29.
[8]卢珍.关于经验模态分解与整体经验模态分解的分离效果差别的探讨[J].科学技术与工程,2011,11(33):8353-8356.(Lu Zhen.A Study on the Different Separation Effect between EMD and EEMD[J].Science Technology and Engineering,2011,11(33):8353-8356.)
[9]刘琦.基于小波分析的轴承故障诊断研究[J].煤,2013,22(7):12-14.(Liu Qi.Research on bearing fault diagnosis based on wavelet[J].Coal,2013,22(7):12-14.)
[10]陈仁祥,汤宝平,吕中亮.基于相关系数的EEMD转子振动信号降噪方法[J].振动.测试与诊断,2012,32(4):542-546.(Chen Ren-xiang,Tang Bao-ping,Lv Zhong-liang.EEMD rotor vibration signal denoising method based on correlation coefficient[J].Journal of Vibration Measurement&Diagnosis,2012,32(4):542-546.)
[11]郭福平,沈书乾,段志宏.基于包络谱分析的滚动轴承内圈故障声发射诊断研究[J].机床与液压,2015,43(17):210-212.(Guo Fu-ping,Shen Shu-qian,Duan Zhi-hong.Study of acoustic emission diagnosis of rolling bearing inner ring fault based on envelope spectrum analysis[J].Machine Tool&Hydraulics,2015,43(17):210-212.)
[12]廖伯瑜.机械故障诊断基础[J].北京:冶金工业出版社,1995(995).(Liao Bai-yu.Mechanical fault diagnosis foundation[J].Beijing:Metallurgical Industry Press,1995(995).)