基于循环自相关的滚动轴承故障特征提取研究
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摘要
滚动轴承在工作过程中产生的振动信号既有周期性又有随机性。周期性信号来源于滚动轴承的周期运转方式,这种周期性本质上是一种近似周期的冲击性振动;随机性信号来源于滚珠的滑移、制造误差等多种因素。因此,对于滚动轴承的故障诊断来说,理论上用循环平稳模型来描述故障特征比单纯用周期性模型描述更加合适。以循环平稳模型为基础,提出一种基于循环自相关的滚动轴承故障特征提取方法,通过理论分析以及滚动轴承故障仿真和试验,证明了循环频率可以反映故障特征频率。用循环自相关函数谱图与包络频谱图进行对比分析,说明在提取滚动轴承故障特征时,利用循环自相关函数法能够很好地抑制噪声。所提出的方法对于滚动轴承故障的精细诊断具有重要的意义。
Vibration signals produced from rolling element bearings in use are both periodic and random. Periodicity comes from almost periodic shock vibrations in nature for its inherent periodic mode of operation,and randomness comes from uncertainty factors such as slip of the balls,manufacturing errors,and so on. The fault model of rolling element bearing is theoretically depicted in cyclostationary model better than in periodic model due to the above reasons. A method of fault feature extraction of rolling element bearing based on cyclic autocorrelation was presented. It proved that cyclic frequency was capable of reflecting the feature frequency of the faulty rolling element bearing by theoretical analysis,computation simulations and experiments. What's more,the cyclic autocorrelation function method could suppress the noise better than the conventional envelope spectrum method could do when extracting fault features of the rolling element bearings. The proposed method was of great significance for the fault fine diagnosis of rolling element bearings.
Vibration signals produced from rolling element bearings in use are both periodic and random. Periodicity comes from almost periodic shock vibrations in nature for its inherent periodic mode of operation,and randomness comes from uncertainty factors such as slip of the balls,manufacturing errors,and so on. The fault model of rolling element bearing is theoretically depicted in cyclostationary model better than in periodic model due to the above reasons. A method of fault feature extraction of rolling element bearing based on cyclic autocorrelation was presented. It proved that cyclic frequency was capable of reflecting the feature frequency of the faulty rolling element bearing by theoretical analysis,computation simulations and experiments. What's more,the cyclic autocorrelation function method could suppress the noise better than the conventional envelope spectrum method could do when extracting fault features of the rolling element bearings. The proposed method was of great significance for the fault fine diagnosis of rolling element bearings.
引文
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[9]明阳.基于循环平稳和盲源分离的滚动轴承故障特征提取方法研究[D].上海:上海交通大学,2013:15-19.MING Y. Study on cyclostationarity and blind source separation-based rolling element bearing fault feature extraction[D]. Shanghai:Shanghai Jiaotong University,2013:15-19.
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[13] ABBOUD D,BAUDIN S,ANTONI J,et al. The spectral analysis of cyclo-non-stationary signals[J]. Mechanical Systems&Signal Processing,2016,75(5):280-300.
[14] KANKAR P K,SHARMA S C,HARSHA S P. Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform[J]. Neurocomputing,2013,110(8):9-17.
[2] ZAKARIA F A,MAIZ S,BADAOUI M E,et al. Firstand second-order cyclostationary signal separation using morphological component analysis[J]. Digital Signal Processing,2016,58(C):134-144.
[3]唐良宝,姜锐红.基于FFT的循环自相关函数算法在滚动轴承故障诊断中的应用[J].轴承,2009(9):43-47.TANG L B,JIANG R H. Cyclic autocorrelation function arithmetic based on FFT and its application in rolling bearing fault diagnosis[J]. Bearing,2009(9):43-47.
[4]朱琳,李贵子,雷小亚,等.基于小波包去噪的循环自相关方法在轴承故障诊断中的应用[J].机械制造与自动化,2016,45(2):153-155.ZHU L,LI G Z,LEI X Y,et al. The application of the denoising wavelet packet based cyclic autocorrelation method in diagnosing the rolling bearing faults[J].Mechanical Manufacturing and Automation,2016,45(2):153-155.
[5]王建国,王戈,王少锋,等.基于小波包自相关的能量算子旋转机械故障诊断[J].河南理工大学学报(自然科学版),2016,35(1):90-94.WANG J G,WANG G,WANG S F,et al. Fault diagnosis of rotating machinery based on autocorrelation and wavelet packet energy demodulation[J]. Journal of Henan Polytechnic University(Natural Science),2016,35(1):90-94.
[6]张龙,胡俊锋,熊国良,等.滚动轴承循环故障特征增强的自相关非局部平均算法[J].机械设计与研究,2017(2):76-80.ZHANG L,HU J F,XIONG G L,et al. Cyclic shock enhancement for rolling bearings by autocorrelation on local means algorithm[J]. Mechanical Design and Research,2017(2):76-80.
[7]王少锋,王戈,王建国,等.基于EMD与自相关的能量算子解调机械故障诊断[J].机械设计与制造,2016(6):174-178.WANG S F,WANG G,WANG J G,et al. Fault diagnosis of machinery based on the EMD and the autocorrelation energy demodulation[J]. Mechanical Design and Manufacturing,2016(6):174-178.
[8]王志坚,王俊元,曾志强,等. MCKD-循环域解调方法在齿轮箱故障诊断中的应用[J].机械传动,2017(1):183-188.WANG Z J,WANG J Y,ZENG Z Q,et al. Application of MCKD-cyclic domain demodulation method in gearbox fault diagnosis[J]. Journal of Mechanical Drive,2017(1):183-188.
[9]明阳.基于循环平稳和盲源分离的滚动轴承故障特征提取方法研究[D].上海:上海交通大学,2013:15-19.MING Y. Study on cyclostationarity and blind source separation-based rolling element bearing fault feature extraction[D]. Shanghai:Shanghai Jiaotong University,2013:15-19.
[10] ANTONI J. Cyclic spectral analysis in practice[J].Mechanical Systems&Signal Processing,2007,21(2):597-630.
[11] ANTONI J. Cyclostationarity by examples[J]. Mechanical Systems&Signal Processing,2009,23(4):987-1036.
[12] RANDALL R B,ANTONI J. Rolling element bearing diagnostics:A tutorial[J]. Mechanical Systems and Signal Processing,2011,25(2):485-520.
[13] ABBOUD D,BAUDIN S,ANTONI J,et al. The spectral analysis of cyclo-non-stationary signals[J]. Mechanical Systems&Signal Processing,2016,75(5):280-300.
[14] KANKAR P K,SHARMA S C,HARSHA S P. Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform[J]. Neurocomputing,2013,110(8):9-17.