基于LCD互近似熵和相关向量机的轴承故障诊断方法
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摘要
针对滚动轴承的故障诊断问题,提出了基于局部特征尺度分解(Local Characteristic-scale Decomposition,LCD)互近似熵(Cross Approximate Entropy,CAE)和相关向量机(Relevance Vector Machine,RVM)的滚动轴承故障诊断方法。该方法首先利用LCD将轴承振动信号分解成若干个具有不同频率成分的内禀尺度分量(Intrinsic Scale Component,ISC);然后通过能量筛选出包含主要故障信息的ISC分量,计算其CAE并作为故障特征向量以体现不同的运行状态;最后将故障特征输入RVM进行故障识别。滚动轴承不同类别和不同损失程度故障实验验证了该方法的有效性。
Aiming at the fault diagnosis problem of rolling bearing,a fault diagnosis method of rolling bearing based on local characteristic-scale decomposition(LCD) cross approximate entropy(CAE) and relevance vector machine(RVM) is proposed. Firstly,the bearing vibration signals is decomposed into several intrinsic scale components(ISC) which with different frequency components. Secondly,some ISCs that contain main fault information are shifted out by the energy analysis criterion and CAE values are calculated as fault feature vectors that could represent the operating conditions of bearings. Finally,the fault feature are put into RVM to identify different faults. The effective of the proposed method is verified by the different fault type and different fault degree of rolling bearing experiment.
Aiming at the fault diagnosis problem of rolling bearing,a fault diagnosis method of rolling bearing based on local characteristic-scale decomposition(LCD) cross approximate entropy(CAE) and relevance vector machine(RVM) is proposed. Firstly,the bearing vibration signals is decomposed into several intrinsic scale components(ISC) which with different frequency components. Secondly,some ISCs that contain main fault information are shifted out by the energy analysis criterion and CAE values are calculated as fault feature vectors that could represent the operating conditions of bearings. Finally,the fault feature are put into RVM to identify different faults. The effective of the proposed method is verified by the different fault type and different fault degree of rolling bearing experiment.
引文
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[10]张宇航,张晔.SVM和RVM对高光谱图像分类的应用潜能分析[J].哈尔滨工业大学学报,2012,44(3):34-39.
[11]周勇,何创新.基于独立特征选择与相关向量机的变载荷轴承故障诊断[J].振动与冲击,2012,31(3):157-161.
[12]吴定海,张培林,张英堂,等.基于时频奇异谱和RVM的柴油机故障诊断研究[J].机械强度,2011,33(3):317-323.
[13]TRAN V T,YANG B S,GU F S,et al.Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis[J].Mechanical Systems and Signal Processing,2013,38(2):601-614.
[14]杨望灿,张培林,王怀光,等.基于奇异值差分谱与改进包络分析的轴承故障特征提取[J].轴承,2013,31(5):49-53.
[2]向丹,葛爽.基于EMD样本熵-LLTSA的故障特征提取方法[J].航空动力学报,2014,29(7):1535-1542.
[3]张良,张前图.基于LCD模糊熵和流行学习的故障特征提取方法[J].机械强度,2016,38(2):225-230.
[4]程军圣,郑近德,杨宇.一种新的非平稳信号分析方法——局部特征尺度分解法[J].振动工程学报,2012,25(2):215-220.
[5]LEI Yaguo,LIN Jing,HE Zhengjia,et al.A review on empirical mode decomposition in fault diagnosis of rotating machinery[J].Mechanical Systems and Signal Processing,2013,35(1/2):108-126.
[6]ANDREADIS I I,GIANNAKAKIS G A,PAPAGRORGIOU C,et al.Detecting complexity abnormalities in dyslexis measuring approximate entropy of electronic signal[C].Engineering in Medicine and Biology Society,Annual International Conference of the IEEE.Minneapolies,2009:6292-6296.
[7]张淑清,胡永涛,李盼,等.基于MEMD互近似熵及FCM聚类的轴承故障诊断方法[J].中国机械工程,2016,26(19):2613-2618.
[8]TIPPING M E.Sparse bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1(3):211-244.
[9]吴震宇,袁惠群,李玲.基于混沌特征和支持向量机的内燃机故障诊断[J].机械强度,2010,32(5):723-728.
[10]张宇航,张晔.SVM和RVM对高光谱图像分类的应用潜能分析[J].哈尔滨工业大学学报,2012,44(3):34-39.
[11]周勇,何创新.基于独立特征选择与相关向量机的变载荷轴承故障诊断[J].振动与冲击,2012,31(3):157-161.
[12]吴定海,张培林,张英堂,等.基于时频奇异谱和RVM的柴油机故障诊断研究[J].机械强度,2011,33(3):317-323.
[13]TRAN V T,YANG B S,GU F S,et al.Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis[J].Mechanical Systems and Signal Processing,2013,38(2):601-614.
[14]杨望灿,张培林,王怀光,等.基于奇异值差分谱与改进包络分析的轴承故障特征提取[J].轴承,2013,31(5):49-53.