粒子滤波在机械密封声发射信号降噪中的应用
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摘要
机械密封声发射信号容易受到环境噪声的干扰,信噪比很低。提出一种基于互补集合经验模态分解(CEEMD)和粒子滤波(PF)相结合的降噪新方法(CEEPF)。该方法首先对采集的声发射信号进行CEEMD分解,利用相关系数原理识别出高频IMF分量后对其进行重构;然后对重构后的信号建立ARIMA模型,将其作为信号的状态方程,再利用小波分解重构思想提取测量噪声,并建立测量方程;最后对重构信号进行粒子滤波,将滤波结果与各低频IMF分量一起重构得到降噪后的声发射信号。结果表明:基于CEEMD与PF的机械密封声发射信号降噪方法能够很好地滤除背景噪声,并且能最大程度保留有效信息。对仿真信号和实验信号分别进行CEEMD小波阈值、标准粒子滤波、CEEPF降噪,发现CEEPF降噪在降噪效果上明显优于其他2种方法。
The acoustic emission signal of the mechanical seal is easily disturbed by the environmental noise,and the signal-to-noise ratio is relatively low.A new de-noising method based on complete ensemble empirical mode decomposition(CEEMD) and particle filter(PF),the CEEPF method was proposed.Firstly,the collected acoustic emission signals was decomposed by the CEEMD,the high-frequency IMF component was identified by using the principle of correlation coefficient,and then it was reconstructed.Then the ARIMA model was built on the reconstructed signals,which is used as the state equation.Then the measurement noise was extracted using the wavelet decomposition and reconstruction idea,and the measurement equation was established.Finally,particle filtering was performed on the reconstructed signal,and the filtered result was reconstructed with each low-frequency IMF component to obtain a noise-reduced acoustic emission signal.The results show that the de-noising method based on CEEMD and PF can effectively filter out background noise of mechanical seal acoustic emission signals and preserve the effective information to the greatest extent.The simulation signal and the test signal were denoised by CEEMD wavelet threshold,standard particle filter,and CEEPF noise reduction,respectively.It is found that the CEEPF method is obviously superior to other two methods in noise reduction effect.
The acoustic emission signal of the mechanical seal is easily disturbed by the environmental noise,and the signal-to-noise ratio is relatively low.A new de-noising method based on complete ensemble empirical mode decomposition(CEEMD) and particle filter(PF),the CEEPF method was proposed.Firstly,the collected acoustic emission signals was decomposed by the CEEMD,the high-frequency IMF component was identified by using the principle of correlation coefficient,and then it was reconstructed.Then the ARIMA model was built on the reconstructed signals,which is used as the state equation.Then the measurement noise was extracted using the wavelet decomposition and reconstruction idea,and the measurement equation was established.Finally,particle filtering was performed on the reconstructed signal,and the filtered result was reconstructed with each low-frequency IMF component to obtain a noise-reduced acoustic emission signal.The results show that the de-noising method based on CEEMD and PF can effectively filter out background noise of mechanical seal acoustic emission signals and preserve the effective information to the greatest extent.The simulation signal and the test signal were denoised by CEEMD wavelet threshold,standard particle filter,and CEEPF noise reduction,respectively.It is found that the CEEPF method is obviously superior to other two methods in noise reduction effect.
引文
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【2】 李晓晖,傅攀,曹伟青,等.机械密封端面接触状态的声发射监测研究[J].振动与冲击,2016,35(8):83-89.LI X H,FU P,CAO W Q,et al.The study of acoustic emission monitoring for contact state of seal end faces[J].Journal of Vibration and Shock,2016,35(8):83-89.
【3】 葛贞笛,傅攀,张尔卿.基于自校正Kalman滤波的液膜密封端面摩擦状态监测技术[J].润滑与密封,2016,41(4):106-110.GE Z D,FU P,ZHANG E Q.Monitoring technique of film seal face friction condition based on Kalman filtering[J].Lubrication Engineering,2016,41(4):106-110.
【4】 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 D Y,DENG A D,LIU Z Y,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.
【5】 HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings:Mathematical,Physical and Engineering Sciences,1998,454:56-78.
【6】 WU Z H,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
【7】 YEH J R,SHIEH J S.Complementary ensemble empirical mode decomposition:a novel noise enhanced data analysis method[J].Advances in Adaptive Data Analysis,2010,2(2):135-156.
【8】 赵迎,乐友喜,黄健良,等.CEEMD与小波变换联合去噪方法研究[J].地球物理学进展,2015,30(6):2870-2877.ZHAO Y,YUE Y X,HUANG J L,et al.CEEMD and wavelet transform jointed de-noising method[J].Progress in Geophysics,2015,30(6):2870-2877.
【9】 赫彬,张雅婷,白艳萍.基于ICA-CEEMD小波阈值的传感器信号去噪[J].振动与冲击,2017,36(4):226-231.HE B,ZHAO Y T,BAI Y P.A method for sensor signal de-noise based on ICA-CEEMD wavelet threshold[J].Journal of Vibration and Shock,2017,36(4):226-231.
【10】 王婷.EMD算法研究及其在信号去噪中的应用[D].哈尔滨:哈尔滨工程大学,2010.
【11】 葛贞笛,张尔卿,傅攀.粒子滤波在机械密封端面接触状态声发射监测中的应用[J].润滑与密封,2015,40(4):95-101.GE Z D,ZHANG E Q,FU P.Application of particle filter on mechanical seal contact condition monitoring of acoustic emission[J].Lubrication Engineering,2015,40(4):95-101.
【12】 黄小平,王岩,繆鹏程.粒子滤波原理及应用:MATLAB仿真[M].北京:电子工业出版社,2017:86-107.
【13】 王淑花.基于时间序列模型的组合预测模型研究[D].秦皇岛:燕山大学,2012.DOI:10.3969/j.issn.0254-0150.2019.05.012文献引用:李飞,孙云岭,田洪祥,等.基于GMR磁传感器的油液铁磁性磨粒在线监测实验研究[J].润滑与密封,2019,44(5):71-76.Cite as:LI Fei,SUN Yunling,TIAN Hongxiang,et al.Experimental research on oil ferromagnetic particles on-line monitoring based on GMR magnetic sensor[J].Lubrication Engineering,2019,44(5):71-76.