基于MOMEDA和包络谱的齿轮微弱故障特征提取
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摘要
复合故障下的齿轮微弱故障易被强故障掩盖而出现漏诊现象,对齿轮复合故障下的微弱故障特征提取进行研究。首先采用多点优化最小熵解卷积调整(Multipoint Optimal Minimum Entropy Deconvolution Adjusted,MOMEDA)作为前置滤波器对原信号进行降噪,增强信号中的周期性冲击成分,然后进行Hilbert变换得到包络谱;通过分析其中明显的频率成分识别故障,实现微弱故障特征的提取。仿真信号和变速器故障诊断实例表明,该方法能有效实现齿轮微弱故障特征提取。
The gear weak fault signal is easy to be covered by the strong fault signal in a complex fault,and there will be missed diagnosis phenomenon. The the feature extraction of weak fault under gear complex failure is studied. Firstly,Multipoint Optimal Minimum Entropy Deconvolution Adjusted( MOMEDA) is used as the pre-filter to denoise the original signal and enhance the periodic impact components in the signal. Then,the Hilbert transform is used to obtain the envelope spectrum. By analyzing the obvious frequency components to identify the fault,the extraction of weak fault characteristics is achieved. Simulation example of fault signal and transmission shows that this method can effectively realize the feature extraction of gear weak fault.
The gear weak fault signal is easy to be covered by the strong fault signal in a complex fault,and there will be missed diagnosis phenomenon. The the feature extraction of weak fault under gear complex failure is studied. Firstly,Multipoint Optimal Minimum Entropy Deconvolution Adjusted( MOMEDA) is used as the pre-filter to denoise the original signal and enhance the periodic impact components in the signal. Then,the Hilbert transform is used to obtain the envelope spectrum. By analyzing the obvious frequency components to identify the fault,the extraction of weak fault characteristics is achieved. Simulation example of fault signal and transmission shows that this method can effectively realize the feature extraction of gear weak fault.
引文
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[10]MCDONALD G L,ZHAO Q,ZUO M J.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems&Signal Processing,2012,33(1):237-255.
[11]王志坚,寇彦飞,王俊元,等.基于最大相关峭度反褶积的齿轮箱复合故障特征提取[J].噪声与振动控制,2017,37(3):173-176.
[12]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(1):461-477.
[13]吕宏强,武志斐,王铁,等.基于AR-MCKD的齿轮点蚀故障特征提取[J].机械传动,2017,41(3):109-113.
[2]钟先友,赵春华,陈保家,等.基于MCKD和包络谱的旋转机械故障诊断方法[J].机械传动,2015,39(7):113-117.
[3]FELDMAN M.Hilbert transform in vibration analysis[J].Mechanical Systems&Signal Processing,2011,25(3):735-802.
[4]JIA X,ZHAO M,DI Y,et al.Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement[J].Journal of Sound&Vibration,2016,386:433-448.
[5]HE D,WANG X,LI S,et al.Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis[J].Mechanical Systems&Signal Processing,2016,81:235-249.
[6]陈向民,于德介,李蓉.基于形态分量分析与阶次跟踪的齿轮箱复合故障诊断方法[J].航空动力学报,2014,29(1):226-231.
[7]WIGGINS R A.Minimum entropy deconvolution[J].Geophysical Prospecting for Petrole,1980,16(1):21-35.
[8]ENDO H,RANDALL R B.Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolutionfilter[J].Mechanical Systems&Signal Processing,2007,21(2):906-919.
[9]王宏超,陈进,董广明.基于最小熵解卷积与稀疏分析的滚动轴承微弱故障特征提取[J].机械工程学报,2013,49(1):89-94.
[10]MCDONALD G L,ZHAO Q,ZUO M J.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems&Signal Processing,2012,33(1):237-255.
[11]王志坚,寇彦飞,王俊元,等.基于最大相关峭度反褶积的齿轮箱复合故障特征提取[J].噪声与振动控制,2017,37(3):173-176.
[12]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(1):461-477.
[13]吕宏强,武志斐,王铁,等.基于AR-MCKD的齿轮点蚀故障特征提取[J].机械传动,2017,41(3):109-113.