基于SVD-AR模型与VPMCD的轴承故障诊断方法
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摘要
针对强噪声背景下振动信号故障特征难以提取的问题,提出了基于奇异值分解的自回归(SVD-AR)模型,用于提取振动信号的特征,并与变量预测模型模式识别(VPMCD)方法相结合应用于轴承故障诊断。对轴承振动信号进行SVD;然后,利用奇异值差分谱对分量信号进行筛选,对能够反映故障信息的分量信号建立AR模型,提取轴承振动信号的特征信息;采用VPMCD对滚动轴承运行状态进行识别。实验证明了方法的合理性和有效性。
Aiming at problem of the failure feature extraction of vibration signal under strong noise background,proposed based on the singular value decomposition and auto regressive( SVD-AR) model,used for feature extraction of the vibration signal and applied for bearing fault diagnosis by combining with the variable predictive model based class discriminate( VPMCD). Firstly,the bearing vibration signal is decomposed by SVD,and then the component signals are screened by using the singular value difference spectrum,and AR model is established for the component signal which can reflect the fault information,extracting characteristic information of bearing vibration signal. Finally,it's used to identify the running state of the rolling bearing by VPMCD. The test of rolling bearing fault diagnosis proves that the method is reasonable and effective.
Aiming at problem of the failure feature extraction of vibration signal under strong noise background,proposed based on the singular value decomposition and auto regressive( SVD-AR) model,used for feature extraction of the vibration signal and applied for bearing fault diagnosis by combining with the variable predictive model based class discriminate( VPMCD). Firstly,the bearing vibration signal is decomposed by SVD,and then the component signals are screened by using the singular value difference spectrum,and AR model is established for the component signal which can reflect the fault information,extracting characteristic information of bearing vibration signal. Finally,it's used to identify the running state of the rolling bearing by VPMCD. The test of rolling bearing fault diagnosis proves that the method is reasonable and effective.
引文
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[2]胥永刚,孟志鹏,陆明,等.基于双树复小波和奇异差分谱的齿轮故障诊断研究[J].振动与冲击,2014,33(1):11-16,23.
[3]朱军,闵祥敏,孔凡让,等.基于分量筛选奇异值分解的滚动轴承故障诊断方法研究[J].振动与冲击,2015(20):61-65.
[4]王奉涛,陈守海,闫达文,等.基于流形—奇异值熵的滚动轴承故障特征提取[J].振动、测试与诊断,2016,14(2):288-294.
[5]郭淑霞,姜颖,刘佳.多传感器系统中基于扩展卡尔曼滤波器的在线故障检测[J].传感器与微系统,2015,34(6):126-129.
[6]赵学智,叶邦彦,陈统坚.基于小波—奇异值分解差分谱的弱故障特征提取方法[J].机械工程学报,2012,48(7):37-48.
[7]陆爽.基于奇异值分解和支持向量机的滚动轴承故障模式识别[J].农业工程学报,2007,23(4):115-119.
[8]王仪明,赵明明,武淑琴,等.基于AR模型的印刷机滚筒扭矩及其振动试验研究[J].振动与冲击,2016,35(3):226-230.
[9]杨宇,杨丽湘,程军圣.基于LMD和AR模型的转子系统故障诊断方法[J].湖南大学学报:自然科学版,2010,37(9):24-28.
[10]Raghuraj R,Lakshminarayanan S.Variable predictive models—A new multivariate classification approach for pattern recognition applications[J].Pattern Recognition,2009,42(1):7-16.
[11]Raghuraj R,Lakshminarayanan S.Variable predictive model based classification algorithm for effective separation of protein structural classes[J].Computational Biology and Chemistry,2008,32(4):302-306.
[12]戴桂平.基于EMD-AR和关联维数的轧机主传动系统故障诊断研究[J].传感器与微系统,2011,30(4):60-62.