基于变分模态分解和去趋势波动分析的柴油机振动信号去噪方法
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摘要
为了解决柴油机工作时其振动信号的背景噪声对状态监测及故障诊断造成干扰这一问题,提出一种基于变分模态分解(VMD)和去趋势波动分析(DFA)的柴油机振动信号去噪方法。该方法首先利用变分模态分解将振动信号分解为若干分量,再利用去趋势波动分析分别计算各个分量的尺度指数,根据尺度指数的值选取具有长程相关性的分量进行信号的重构,以消除振动信号中噪声。将该方法应用于仿真信号和柴油机故障振动信号中,取得了良好的消噪效果。
In order to solve the problem that the background noise of diesel engine vibration signals interferes with operating condition monitoring and fault diagnosis,a vibration signal denoising method based on variational mode decomposition(VMD)and detrended fluctuation analysis(DFA)was proposed.The variational mode decomposition was used to decompose the vibration signal into several components,and then the detrended fluctuation analysis was used to calculate the scale index of each component to select the components with a long-range correlation for signal reconstruction,thus eliminating the noise in the vibration signals.The proposed method was applied to the simulation signals and diesel engine fault vibration signals,and a good denosing effect was obtained.
In order to solve the problem that the background noise of diesel engine vibration signals interferes with operating condition monitoring and fault diagnosis,a vibration signal denoising method based on variational mode decomposition(VMD)and detrended fluctuation analysis(DFA)was proposed.The variational mode decomposition was used to decompose the vibration signal into several components,and then the detrended fluctuation analysis was used to calculate the scale index of each component to select the components with a long-range correlation for signal reconstruction,thus eliminating the noise in the vibration signals.The proposed method was applied to the simulation signals and diesel engine fault vibration signals,and a good denosing effect was obtained.
引文
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[10]AN X L,TANG Y J.Denoising of hydropower unit vibration signal based on variational mode decomposition and approximate entropy[J].Transactions of the Institute of Measurement and Control,2016,38(3):282-292.
[11]LI Z P,CHEN J L,ZI Y Y.Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive[J].Mechanical Systems and Signal Processing,2017,85:512-529.
[12]PENG C K,HAVLIN S,STANLEY H E,et al.Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series.[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,1995,5(1):82-87.
[13]肖涵,吕勇,王涛.齿轮振动信号的去趋势波动分析及其在故障分类中的应用[J].振动工程学报,2015(2):331-336.XIAO H,LY,WANG T.Detrended fluctuation analysis to gears vibration signals and its application in fault classification[J].Journal of Vibration Engineering,2015,28(2):331-336.
[14]MOURA E P D,VIEIRA A P,IRMAO M A S,et al.Applications of detrended-fluctuation analysis to gearbox fault diagnosis[J].Mechanical Systems&Signal Processing,2009,23(3):682-689.
[15]边杰.基于遗传算法参数优化的变分模态分解结合1.5维谱的轴承故障诊断[J].推进技术,2017,38(7):1618-1624.BIAN J.Fault diagnosis of bearing combining parameter optimized variational mode decomposition based on genetic algorithm with 1.5-dimensional spectrum[J].Journal of Propulsion Technology,2017,38(7):1618-1624.
[16]肖云魁.汽车故障诊断学.[M].2版.北京:北京理工大学出版社,2006.
[2]王宏强,尚春阳,高瑞鹏,等.基于小波系数变换的小波阈值去噪算法改进[J].振动与冲击,2011,30(10):165-168.WANG H Q,SHANG C Y,GAO R P,et al.An improvement of wavelet shrinkage denoising via wavelet coefficient transformation[J].Journal of Vibration&Shock,2011,34(10):165-168.
[3]周智,朱永生,张优云,等.基于EMD间隔阈值消噪与极大似然估计的滚动轴承故障诊断方法[J].振动与冲击,2013,32(9):155-159.ZHOU Z,ZHU Y S,ZHANG Y Y,et al.Fault diagnosis of rolling bearings based on EMD interval-threshold denoising and maximum likelihood estimation[J].Journal of Vibration&Shock,2013,32(9):155-159.
[4]HUANG N E,SHEN Z,LONG S R.A new view of nonlinear water waves:the Hilbert spectrum[J].Annu.Rev.Fluid Mech.,1999,31:417-457.
[5]邵忍平,曹精明,李永龙.基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J].振动与冲击,2012,31(8):96-101.SHAO R P,CAO J M,LI Y L.Gear fault pattern identification and diagnosis using time-frequency analysis and wavelet threshold de-noising based on EMD[J].Journal of Vibration&Shock,2012,31(8):96-101.
[6]DRAGOMIRETSKIY K,ZOSSO D.Variational mode decomposition[J].IEEE Transactions on Signal Processing,2013,62(3):531-544.
[7]唐贵基,王晓龙.变分模态分解方法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):638-648.TANG G J,WANG X L.Variational modal decomposition method and its application on incipient fault diagnosis of rolling bearing[J].Journal of Vibration Engineering,2016,29(4):638-648.
[8]ZHAO C,FENG Z P.Application of multi-domain sparse features for fault identification of planetary gearbox[J].Measurement,2017,104:169-179.
[9]AN X L,TANG Y J.Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine[J].Transactions of the Institute of Measurement and Control,2017,39(7):1000-1006.
[10]AN X L,TANG Y J.Denoising of hydropower unit vibration signal based on variational mode decomposition and approximate entropy[J].Transactions of the Institute of Measurement and Control,2016,38(3):282-292.
[11]LI Z P,CHEN J L,ZI Y Y.Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive[J].Mechanical Systems and Signal Processing,2017,85:512-529.
[12]PENG C K,HAVLIN S,STANLEY H E,et al.Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series.[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,1995,5(1):82-87.
[13]肖涵,吕勇,王涛.齿轮振动信号的去趋势波动分析及其在故障分类中的应用[J].振动工程学报,2015(2):331-336.XIAO H,LY,WANG T.Detrended fluctuation analysis to gears vibration signals and its application in fault classification[J].Journal of Vibration Engineering,2015,28(2):331-336.
[14]MOURA E P D,VIEIRA A P,IRMAO M A S,et al.Applications of detrended-fluctuation analysis to gearbox fault diagnosis[J].Mechanical Systems&Signal Processing,2009,23(3):682-689.
[15]边杰.基于遗传算法参数优化的变分模态分解结合1.5维谱的轴承故障诊断[J].推进技术,2017,38(7):1618-1624.BIAN J.Fault diagnosis of bearing combining parameter optimized variational mode decomposition based on genetic algorithm with 1.5-dimensional spectrum[J].Journal of Propulsion Technology,2017,38(7):1618-1624.
[16]肖云魁.汽车故障诊断学.[M].2版.北京:北京理工大学出版社,2006.