基于Birge-Massart阈值降噪与EEMD及谱峭度的滚动轴承故障特征提取
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摘要
针对传统共振解调方法易受噪声干扰导致故障特征提取效果不佳的问题,提出了一种基于Birge-Massart策略的阈值降噪与集成经验模态分解(EEMD)和快速谱峭度算法相结合的滚动轴承故障特征提取方法。对原始故障信号进行EEMD并采用峭度准则筛选出含有故障信息的本征模态函数(IMF)分量;采用Birge-Massart策略和快速谱峭度对故障信号进行滤波降噪;对滤波后信号进行Hilbert包络解调,提取轴承故障特征。采用该方法分别对仿真信号和实验信号进行特征提取,结果表明该方法可以有效提高故障信号信噪比,清晰准确地获取轴承内、外圈故障的频率特征。利用峭度因子准则筛选IMF分量能有效保留原始故障信号中的冲击特征,去除无关IMF分量的影响。
For the difficulty of extracting fault feature by adopting the traditional resonance demodulation method due to noise interference,an effective fault feature extraction method of rolling bearing was proposed by integrating Birge-Massart-based threshold de-noising strategy with ensemble empirical mode decomposition(EEMD)and fast spectral kurtosis algorithm.EEMD was applied to decompose original fault signals and then extract intrinsic mode function(IMF)components containing fault information by kurtosis criterion.Birge-Massart strategy and fast spectral kurtosis were employed to filter and denoise fault signals.The fault features of the filtered signals were extracted by Hilbert envelope demodulation.Through the fault feature extraction of the simulated signals and experimental signals based on this developed method,the results demonstrate that the proposed method is effective to improve the signal-to-noise ratio of fault signals and to acquire the frequency feature of inner and outer ring faults.Using kurtosis factor criterion to select IMF components can effectively retain the impact features of the original fault signal and remove the influence of irrelevant IMF components.
For the difficulty of extracting fault feature by adopting the traditional resonance demodulation method due to noise interference,an effective fault feature extraction method of rolling bearing was proposed by integrating Birge-Massart-based threshold de-noising strategy with ensemble empirical mode decomposition(EEMD)and fast spectral kurtosis algorithm.EEMD was applied to decompose original fault signals and then extract intrinsic mode function(IMF)components containing fault information by kurtosis criterion.Birge-Massart strategy and fast spectral kurtosis were employed to filter and denoise fault signals.The fault features of the filtered signals were extracted by Hilbert envelope demodulation.Through the fault feature extraction of the simulated signals and experimental signals based on this developed method,the results demonstrate that the proposed method is effective to improve the signal-to-noise ratio of fault signals and to acquire the frequency feature of inner and outer ring faults.Using kurtosis factor criterion to select IMF components can effectively retain the impact features of the original fault signal and remove the influence of irrelevant IMF components.
引文
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[18]WANG Yanxue,XIANG Jiawei,MARKERT R,et al.Spectral kurtosis for fault detection,diagnosis and prognostics of rotating machines:a review with applications[J].Mechanical Systems and Signal Processing,2016,66/67(1):679-698.
[19]剡昌锋,苑浩,王鑫,等.点接触弹流润滑条件下的深沟球轴承表面局部缺陷动力学建模[J].振动与冲击,2016,35(14):61-70.YAN Changfeng,YUAN Hao,WANG Xin,et al.Dynamic modeling of surface defects in deep groove ball bearings under point-contact elastohydrodynamic lubrication conditions[J].Journal of Vibration and Shock,2016,35(14):61-70.(in Chinese)
[2]ANTONI J.The spectral kurtosis:a useful tool for characterising non-stationary signals[J].Mechanical Systems and Signal Processing,2006,20(2):282-307.
[3]ANTONI J,RANDALL R B.The spectral kurtosis:application to the vibratory surveillance and diagnostics of rotating machines[J].Mechanical Systems and Signal Processing,2006,20(2):308-331.
[4]ANTONI J.Fast computation of the kurtogram for the detection of transient faults[J].Mechanical Systems and Signal Processing,2007,21(1):108-124.
[5]蔡艳平,李艾华,石林锁,等.基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J].振动与冲击,2011,30(2):167-172.CAI Yanping,LI Aihua,SHI Linsuo,et al.Improved envelope spectrum analysis of rolling bearing fault detection based on EMD and spectral kurtosis[J].Journal of Vibration and Shock,2011,30(2):167-172.(in Chinese)
[6]张志刚,石晓辉,施全,等.基于改进EMD和谱峭度法滚动轴承故障特征提取[J].振动、测试与诊断,2013,33(3):478-482.ZHANG Zhigang,SHI Xiaohui,SHI Quan,et al.Fault feature extraction of rolling bearing based on improved emd and spectral kurtosis[J].Journal of Vibration,Measurement and Diagnosis,2013,33(3):478-482.(in Chinese)
[7]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.
[8]周智,朱永生,张优云,等.基于EEMD和共振解调的滚动轴承自适应故障诊断[J].振动与冲击,2013,32(2):76-80.ZHOU Zhi,ZHU Yongsheng,ZHANG Youyun,et al.A-daptive fault diagnosis of rolling bearing based on EEMDand resonance demodulation[J].Journal of Vibration and Shock,2013,32(2):76-80.(in Chinese)
[9]李宏坤,杨蕊,任远杰,等.利用粒子滤波与谱峭度的滚动轴承故障诊断[J].机械工程学报,2017,53(3):63-72.LI Hongkun,YANG Rui,REN Yuanjie,et al.Fault diagnosis of rolling bearings using particle filter and spectral kurtosis[J].Journal of Mechanical Engineering,2017,53(3):63-72.(in Chinese)
[10]SIDHIK S.Comparative study of Birge-Massart strategy and unimodal thresholding for image compression using wavelet transform[J].Optik-International Journal for Light and Electron Optics,2015,126(24):5952-5955.
[11]唐炬,董玉林,樊雷,等.基于Hankel矩阵的复小波-奇异值分解法提取局部放电特征信息[J].中国电机工程学报,2015,35(7):1808-1817.TANG Ju,DONG Yulin,FAN Lei,et al.Extraction of partial discharge characteristic information based on hankel matrix complex wavelet-singular value decomposition method[J].Proceedings of the CSEE,2015,35(7):1808-1817.(in Chinese)
[12]GOPI V P,PAVITHRAN M,NISHANTH T,et al.A novel wavelet based denoising algorithm using level dependent thresholding[C]∥Proceedings of International Conference on Electronics&Communication Systems.Coimbatore,India:IEEE,2014:1-6.
[13]FENG Y,THANAGASUNDRAM S,SCHLINDWEIN FS.Discrete wavelet-based thresholding study on acoustic emission signals to detect bearing defect on a rotating machine[C]∥Proceedings of Thirteenth International Congress on Sound&Vibration.Vienna,Austria:International Institute of Acoustics and Vibration,2006:1-8.
[14]ZHANG Xiaoyuan,ZHOU Jianzhong.Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines[J].Mechanical Systems and Signal Processing,2013,41(1/2):127-140.
[15]谷朝健,马增强,王建东,等.多通道相关-自适应轴承故障诊断方法[J].航空动力学报,2018,33(7):1750-1757.GU Chaojian,MA Zengqiang,WANG Jiandong,et al.A-daptive bearing fault diagnosis method of multi-channel correlation[J].Journal of Aerospace Power,2018,33(7):1750-1757.(in Chinese)
[16]CUI Huimin,ZHAO Ruimei,HOU Yanli.Improved threshold denoising method based on wavelet transform[J].Physics Procedia,2012,33(1):1354-1359.
[17]BIRGé L,MASSART P.From model selection to adaptive estimation[M].New York:Springer,1997.
[18]WANG Yanxue,XIANG Jiawei,MARKERT R,et al.Spectral kurtosis for fault detection,diagnosis and prognostics of rotating machines:a review with applications[J].Mechanical Systems and Signal Processing,2016,66/67(1):679-698.
[19]剡昌锋,苑浩,王鑫,等.点接触弹流润滑条件下的深沟球轴承表面局部缺陷动力学建模[J].振动与冲击,2016,35(14):61-70.YAN Changfeng,YUAN Hao,WANG Xin,et al.Dynamic modeling of surface defects in deep groove ball bearings under point-contact elastohydrodynamic lubrication conditions[J].Journal of Vibration and Shock,2016,35(14):61-70.(in Chinese)