基于改进信息图与MOMEDA的滚动轴承故障特征提取
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摘要
为解决最大相关峭度解卷积存在的故障周期需要预先设置等问题,提出多点优化最小熵解卷积修正(MOMEDA)用于增强轴承故障信号,并应用改进信息图降低噪声对其多点峭度谱的干扰。通过引入轴承故障与正常状态下谱负熵的比值关系,优化信息图中平均谱负熵算法,提出基于滤波器组的改进信息图方法;构建带通滤波器进行滤波降噪,并通过MOMEDA多点峭度谱识别故障周期;应用MOMEDA增强滤波信号中的故障周期性脉冲成分,并通过平方包络谱提取微弱故障特征。试验表明,较之信息图等方法,改进信息图的降噪效果较突出,可有效提高故障周期的识别度,实现MOMEDA自适应增强故障信号。
To solve the selection problem of the faulted period in the maximum correlated kurtosis deconvolution method, multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) was introduced to enhance the faulted signal. Furthermore, improved infogram was combined to denoise for MOMEDA multipoint kurtosis(MKurt) spectrum. First, the spectral negentropy ratio of both faulted and healthy signals was considered to improve the average spectral negentropy algorithm. As a result, improved infogram based on filter banks was proposed. Second, in order to correctly identify the faulted period in the MOMEDA MKurt spectrum, the proposed method was employed to construct an optimal band-pass filter for noise reduction. Third, the weak fault characteristics were extracted by means of the square envelope spectrum after the MOMEDA being applied to enhance periodic impulses in the filtered signal of rolling bearings. The results of measured fault signals show that the improved infogram is better on denoising. Moreover, it can effectively improve the faulted period's identification and help MOMEDA adaptively with enhancing faulted signals.
To solve the selection problem of the faulted period in the maximum correlated kurtosis deconvolution method, multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) was introduced to enhance the faulted signal. Furthermore, improved infogram was combined to denoise for MOMEDA multipoint kurtosis(MKurt) spectrum. First, the spectral negentropy ratio of both faulted and healthy signals was considered to improve the average spectral negentropy algorithm. As a result, improved infogram based on filter banks was proposed. Second, in order to correctly identify the faulted period in the MOMEDA MKurt spectrum, the proposed method was employed to construct an optimal band-pass filter for noise reduction. Third, the weak fault characteristics were extracted by means of the square envelope spectrum after the MOMEDA being applied to enhance periodic impulses in the filtered signal of rolling bearings. The results of measured fault signals show that the improved infogram is better on denoising. Moreover, it can effectively improve the faulted period's identification and help MOMEDA adaptively with enhancing faulted signals.
引文
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[2]张云强,张培林,王怀光,等.基于双时域微弱故障特征增强的轴承早期故障智能识别[J].机械工程学报,2016,52(21):96-103.ZHANG Yunqiang,ZHANG Peilin,WANG Huaiguang,et al.Rolling bearing early fault intelligence recognition based on weak fault feature enhancement in time-time domain[J].Journal of Mechanical Engineering,2016,52(21):96-103.
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[4]唐贵基,王晓龙.最大相关峭度解卷积结合1. 5维谱的滚动轴承早期故障特征提取方法[J].振动与冲击,2015,34(12):79-84.TANG Guiji,WANG Xiaolong. Feature extraction for rolling bearing incipient fault based on maximum correlated kurtosis deconvolution and 1. 5 dimension spectrum[J]. Journal of Vibration and Shock,2015,34(12):79-84.
[5]刘尚坤,唐贵基,何玉灵. Teager能量算子结合MCKD的滚动轴承早期故障识别[J].振动与冲击,2016,35(15):98-102.LIU Shangkun, TANG Guiji, HE Yuling. Incipient fault diagnosis of rolling bearings based on teager energy operator and MCKD[J]. Journal of Vibration and Shock,2016,35(15):98-102.
[6]夏均忠,赵磊,白云川,等.基于MCKD和VMD的滚动轴承微弱故障特征提取[J].振动与冲击,2017,36(20):78-83.XIA Junzhong,ZHAO Lei,BAI Yunchuan,et al. Rolling element bearing weak fault diagnosis based on MCKD and VMD[J]. Journal of Vibration and Shock,2017,36(20):78-83.
[7]钟先友,赵春华,陈保家,等.基于MCKD和重分配小波尺度谱的旋转机械复合故障诊断研究[J].振动与冲击,2015,34(7):156-161.ZHONG Xianyou,ZHAO Chunhua,CHEN Baojia,et al.Rotating machinery fault diagnosis based on maximum correlation kurtosis deconvolution and reassigned wavelet scalogram[J]. Journal of Vibration and Shock,2015,34(7):156-161.
[8]吕中亮,汤宝平,周亿,等.基于网格搜索法优化最大相关峭度反卷积的滚动轴承早期故障诊断方法[J].振动与冲击,2016,35(15):29-34.LZhongliang,TANG Baoping,ZHOU Yi,et al. Rolling bearing early fault diagnosis based on maximum correlated kurtosis deconvolution optimized with grid search algorithm[J]. Journal of Vibration and Shock,2016,35(15):29-34.
[9] MCDONALD G L,ZHAO Q. Multipoint optimal minimum entropy deconvolution and convolution fix:application to vibration fault detection[J]. Mechanical Systems and Signal Processing,2017,82:461-477.
[10]王宏超,陈进,董广明,等.基于快速kurtogram算法的共振解调方法在滚动轴承故障特征提取中的应用[J].振动与冲击,2013,32(1):35-38.WANG Hongchao,CHEN Jin,DONG Guangming,et al.Application of resonance demodulation in rolling bearing fault feature extraction based on fast computation of kurtogram[J].Journal of Vibration and Shock,2013,32(1):35-38.
[11]张龙,熊国良,黄文艺.复小波共振解调频带优化方法和新指标[J].机械工程学报,2015,51(3):129-138.ZHANG Long, XIONG Guoliang, HUANG Wenyi. New procedure and index for the parameter optimization of complex wavelet based resonance demodulation[J]. Journal of Mechanical Engineering,2015,51(3):129-138.
[12]ANTONI J. The infogram:entropic evidence of the signature of repetitive transients[J]. Mechanical Systems and Signal Processing,2016,74:73-94.
[13] ANTONI J. Fast computation of the Kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing,2007,21(1):108-124.
[14]BARSZCZ T,JABLONSKI A. A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram[J]. Mechanical Systems and Signal Processing,2011,25(1):431-451.
[15]夏均忠,于明奇,黄财,等.基于Infogram的共振解调方法在滚动轴承故障特征提取中的应用[J].振动与冲击,2018,37(12):29-34.XIA Junzhong,YU Mingqi,HUANG Cai,et al. Application of resonance demodulation in rolling bearing fault feature extraction based on Infogram[J]. Journal of Vibration and Shock,2018,37(12):29-34.
[16] WANG Dong. An extension of the infograms to novel Bayesian inference for bearing fault feature identification[J]. Mechanical Systems and Signal Processing,2016,80:19-30.
[17]LI Chuan,CABRERA D,CERRADA M,et al. Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram[J]. Mechanical Systems and Signal Processing,2016,76/77:157-173.
[18] The Case Western Reserve University Bearing Data Center Website[EB/OL].(2013-05-12). http:∥www. eecs.case. edu/laboratory/bearing.
[19]ABBOUD D,ANTONI J,SIEG-ZIEBA S,et al. Envelope analysis of rotating machine vibrations invariable speed conditions:a comprehensive treatment[J]. Mechanical Systems and Signal Processing,2017,84:200-226.
[20] MCFADDEN P D,SMITH J D. Model for the vibration produced by a single point defect in a rolling element bearing[J]. Journal of Sound and Vibration,1984,96(1):69-82.
[21]夏均忠,于明奇,黄财,等.基于VMD和Infogram的滚动轴承故障特征提取[J].振动与冲击,2017,36(22):111-117.XIA Junzhong,YU Mingqi,HUANG Cai,et al. Fault feature extraction of rolling element bearing based on VMD and Infogram[J]. Journal of Vibration and Shock,2017,36(22):111-117.
[2]张云强,张培林,王怀光,等.基于双时域微弱故障特征增强的轴承早期故障智能识别[J].机械工程学报,2016,52(21):96-103.ZHANG Yunqiang,ZHANG Peilin,WANG Huaiguang,et al.Rolling bearing early fault intelligence recognition based on weak fault feature enhancement in time-time domain[J].Journal of Mechanical Engineering,2016,52(21):96-103.
[3]MCDONALD G L,ZHAO Q,ZUO M J. Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems and Signal Processing,2012,33(1):237-255.
[4]唐贵基,王晓龙.最大相关峭度解卷积结合1. 5维谱的滚动轴承早期故障特征提取方法[J].振动与冲击,2015,34(12):79-84.TANG Guiji,WANG Xiaolong. Feature extraction for rolling bearing incipient fault based on maximum correlated kurtosis deconvolution and 1. 5 dimension spectrum[J]. Journal of Vibration and Shock,2015,34(12):79-84.
[5]刘尚坤,唐贵基,何玉灵. Teager能量算子结合MCKD的滚动轴承早期故障识别[J].振动与冲击,2016,35(15):98-102.LIU Shangkun, TANG Guiji, HE Yuling. Incipient fault diagnosis of rolling bearings based on teager energy operator and MCKD[J]. Journal of Vibration and Shock,2016,35(15):98-102.
[6]夏均忠,赵磊,白云川,等.基于MCKD和VMD的滚动轴承微弱故障特征提取[J].振动与冲击,2017,36(20):78-83.XIA Junzhong,ZHAO Lei,BAI Yunchuan,et al. Rolling element bearing weak fault diagnosis based on MCKD and VMD[J]. Journal of Vibration and Shock,2017,36(20):78-83.
[7]钟先友,赵春华,陈保家,等.基于MCKD和重分配小波尺度谱的旋转机械复合故障诊断研究[J].振动与冲击,2015,34(7):156-161.ZHONG Xianyou,ZHAO Chunhua,CHEN Baojia,et al.Rotating machinery fault diagnosis based on maximum correlation kurtosis deconvolution and reassigned wavelet scalogram[J]. Journal of Vibration and Shock,2015,34(7):156-161.
[8]吕中亮,汤宝平,周亿,等.基于网格搜索法优化最大相关峭度反卷积的滚动轴承早期故障诊断方法[J].振动与冲击,2016,35(15):29-34.LZhongliang,TANG Baoping,ZHOU Yi,et al. Rolling bearing early fault diagnosis based on maximum correlated kurtosis deconvolution optimized with grid search algorithm[J]. Journal of Vibration and Shock,2016,35(15):29-34.
[9] MCDONALD G L,ZHAO Q. Multipoint optimal minimum entropy deconvolution and convolution fix:application to vibration fault detection[J]. Mechanical Systems and Signal Processing,2017,82:461-477.
[10]王宏超,陈进,董广明,等.基于快速kurtogram算法的共振解调方法在滚动轴承故障特征提取中的应用[J].振动与冲击,2013,32(1):35-38.WANG Hongchao,CHEN Jin,DONG Guangming,et al.Application of resonance demodulation in rolling bearing fault feature extraction based on fast computation of kurtogram[J].Journal of Vibration and Shock,2013,32(1):35-38.
[11]张龙,熊国良,黄文艺.复小波共振解调频带优化方法和新指标[J].机械工程学报,2015,51(3):129-138.ZHANG Long, XIONG Guoliang, HUANG Wenyi. New procedure and index for the parameter optimization of complex wavelet based resonance demodulation[J]. Journal of Mechanical Engineering,2015,51(3):129-138.
[12]ANTONI J. The infogram:entropic evidence of the signature of repetitive transients[J]. Mechanical Systems and Signal Processing,2016,74:73-94.
[13] ANTONI J. Fast computation of the Kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing,2007,21(1):108-124.
[14]BARSZCZ T,JABLONSKI A. A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram[J]. Mechanical Systems and Signal Processing,2011,25(1):431-451.
[15]夏均忠,于明奇,黄财,等.基于Infogram的共振解调方法在滚动轴承故障特征提取中的应用[J].振动与冲击,2018,37(12):29-34.XIA Junzhong,YU Mingqi,HUANG Cai,et al. Application of resonance demodulation in rolling bearing fault feature extraction based on Infogram[J]. Journal of Vibration and Shock,2018,37(12):29-34.
[16] WANG Dong. An extension of the infograms to novel Bayesian inference for bearing fault feature identification[J]. Mechanical Systems and Signal Processing,2016,80:19-30.
[17]LI Chuan,CABRERA D,CERRADA M,et al. Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram[J]. Mechanical Systems and Signal Processing,2016,76/77:157-173.
[18] The Case Western Reserve University Bearing Data Center Website[EB/OL].(2013-05-12). http:∥www. eecs.case. edu/laboratory/bearing.
[19]ABBOUD D,ANTONI J,SIEG-ZIEBA S,et al. Envelope analysis of rotating machine vibrations invariable speed conditions:a comprehensive treatment[J]. Mechanical Systems and Signal Processing,2017,84:200-226.
[20] MCFADDEN P D,SMITH J D. Model for the vibration produced by a single point defect in a rolling element bearing[J]. Journal of Sound and Vibration,1984,96(1):69-82.
[21]夏均忠,于明奇,黄财,等.基于VMD和Infogram的滚动轴承故障特征提取[J].振动与冲击,2017,36(22):111-117.XIA Junzhong,YU Mingqi,HUANG Cai,et al. Fault feature extraction of rolling element bearing based on VMD and Infogram[J]. Journal of Vibration and Shock,2017,36(22):111-117.