基于格点搜索法的MOMEDA在滚动轴承故障特征提取中的应用
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摘要
针对背景噪声下故障轴承产生的周期性脉冲特征微弱难以提取问题,提出多点优化最小熵解卷积修正(MOMEDA)方法,并利用格点搜索法解决其滤波器设置需人工干预问题。首先以频域谱负熵为寻优目标,利用格点搜索法迭代求解MOMEDA滤波器最优阶数;其次应用该参数寻优方法下的MOMEDA对仿真信号和轴承内圈故障信号中的周期性脉冲成分进行增强,并通过平方包络谱提取微弱故障特征;然后应用受试者工作特征(ROC)曲线评估该方法的灵敏性和特异性。该方法可有效增强故障脉冲成分,且具有良好的灵敏性和特异性。
Since it is difficult to extract periodic impulse caused by faulty bearing in background noise,the paper proposes multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) method and solves the filter setting problem with grid search method. Firstly,it takes spectral negentropy in frequency domain as optimization object,and solves optimal order of filter with grid search method. Then,it intensifies the periodic impulse in simulated signal and fault signal of bearing inside track with MOMEDA,and extracts weak fault feature with square envelope spectrum. Finally,it evaluates the sensitivity and specificity of this method with receiver operating characteristic(ROC) curve. This method can intensify fault impulse effectively,and it has good sensitivity and specificity.
Since it is difficult to extract periodic impulse caused by faulty bearing in background noise,the paper proposes multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) method and solves the filter setting problem with grid search method. Firstly,it takes spectral negentropy in frequency domain as optimization object,and solves optimal order of filter with grid search method. Then,it intensifies the periodic impulse in simulated signal and fault signal of bearing inside track with MOMEDA,and extracts weak fault feature with square envelope spectrum. Finally,it evaluates the sensitivity and specificity of this method with receiver operating characteristic(ROC) curve. This method can intensify fault impulse effectively,and it has good sensitivity and specificity.
引文
[1]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):13-19.
[2]MING Y,CHEN J,DONG G M,et al.Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum[J].Mechanical Systems and Signal Processing,2011,25:1773-1785.
[3]ENDO H,RANDALL R B.Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter[J].Mechanical Systems and Signal Processing,2007,21(2):906-919.
[4]BARSZCZ T,SAWALHI N.Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution[J].Archives of Acoustics,2012,37(2):131-141.
[5]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,2012,33:237-255.
[6]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.
[7]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.
[8]LAHA S K.Enhancement of fault diagnosis of rolling element bearing using maximum kurtosis fast nonlocal means denoising[J].Measurement,2017,100:157-163.
[9]SAIDI L,ALI J B,BENBOUZID M,et al.The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines[J].ISA Transactions,2016,63:436-447.
[10]ANTONI J.The infogram:entropic evidence of the signature of repetitive transients[J].Mechanical Systems and Signal Processing,2016,74:73-94.
[11]SMITH W A,RANDALL R B.Rolling element bearing diagnostics using the Case Western Reserve University data:a benchmark study[J].Mechanical Systems and Signal Processing,2015,64/65:100-131.
[12]The case western reserve university bearing data center Website[EB/OL].(2013-05-12)[2017-05-20].http://www.eecs.case.edu/laboratory/bearing.
[2]MING Y,CHEN J,DONG G M,et al.Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum[J].Mechanical Systems and Signal Processing,2011,25:1773-1785.
[3]ENDO H,RANDALL R B.Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter[J].Mechanical Systems and Signal Processing,2007,21(2):906-919.
[4]BARSZCZ T,SAWALHI N.Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution[J].Archives of Acoustics,2012,37(2):131-141.
[5]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,2012,33:237-255.
[6]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.
[7]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.
[8]LAHA S K.Enhancement of fault diagnosis of rolling element bearing using maximum kurtosis fast nonlocal means denoising[J].Measurement,2017,100:157-163.
[9]SAIDI L,ALI J B,BENBOUZID M,et al.The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines[J].ISA Transactions,2016,63:436-447.
[10]ANTONI J.The infogram:entropic evidence of the signature of repetitive transients[J].Mechanical Systems and Signal Processing,2016,74:73-94.
[11]SMITH W A,RANDALL R B.Rolling element bearing diagnostics using the Case Western Reserve University data:a benchmark study[J].Mechanical Systems and Signal Processing,2015,64/65:100-131.
[12]The case western reserve university bearing data center Website[EB/OL].(2013-05-12)[2017-05-20].http://www.eecs.case.edu/laboratory/bearing.