带通滤波器参数(中心频率和带宽)选取是共振解调的关键,针对快速峭度图找寻的中心频率偏大、带宽过宽的问题,提出Infogram(信息图)用于确定滤波器参数;并利用变分模态分解(Variational Mode Decomoposition, VMD)预先对信号进行重构,以减少噪声对信息图的影响,增强其应用效果。首先对轴承故障振动信号进行变分模态分解得到有限个模态分量,然后根据模态选取准则确定包含故障信息较多的模态分量进行信号重构,再应用信息图确定最佳共振频带的中心频率和带宽,最后对重构信号进行带通滤波和包络谱分析,识别轴承故障特征频率。仿真分析和轴承外圈模拟故障试验验证了该方法的有效性。
Abstract
It is crucial to select the band-pass filter parameters (center frequency and bandwidth) in resonance demodulation. To overcome a problem occurred in fast kurtogram that both the center frequency and bandwidth are too large, infogram was presented to select the filter parameters. And the signal was reconstructed beforehand to reduce the influence of noise on infogram for added effect. Firstly, roller bearing fault vibration signals were decomposed into a finite number of modes. Secondly, the modes contained rich fault information were selected according to the selection criterion of modes, then the center frequency and bandwidth of optimal resonance frequency band were selected with the help of infogram. Finally, fault feature frequency were obtained by band filter and envelope demodulation. The simulated signal and the measured outer fault signal of rolling bearing show that the proposed method is effective for fault feature extraction of rolling bearing.
关键词
滚动轴承 /
特征提取 /
快速峭度图 /
变分模态分解 /
信息图
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Key words
rolling element bearing /
fault feature extraction /
fast kurtogram /
VMD /
infogram
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参考文献
[1] 夏均忠,刘远宏,李树珉,等. 应用Hilbert变换和ZFFT提取变速器齿轮故障特征[J]. 振动与冲击,2013,32(6):63-66.
XIA Jun-zhong,LIU Yuan-hong,LI Shu-min,et. al. Gearing fault detection using Hilbert transform and ZFFT[J]. Journal of vibration and shock, 2013, 32(6): 63-66.
[2] 王宏超,陈进,董广明,等. 基于快速kurtogram算法的共振解调方法在滚动轴承故障特征提取中的应用[J]. 振动与冲击,2013,32(1):35-38.
WANG Hong-chao,CHEN Jin,DONG Gung-ming,et. al. Application of resonance demodulation in rolling bearing fault feature extraction based on fast computation of kurtogram[J]. Journal of vibration and shock, 2013, 32(1): 35-38.
[3] Antoni J. The spectral kurtosis: a useful tool for characterizing nonstationary signals[J]. Mechanical Systems and Signal Processing, 2006, 20(2): 282-307.
[4] Antoni J,Randall R B. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines[J]. Mechanical Systems and Signal Processing, 2006, 20(2): 308-331.
[5] Antoni J. Fast computation of the Kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1): 108-124.
[6] Guo Wei,Tse P W,Djordjevich A. Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition[J]. Measurement, 2012, 45(5): 1308-1322.
[7] Lei Y G,Lin J,He Z J,et al. Application of an improved kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2011, 25(5): 1738-1749.
[8] Barszcz T,Jablonski A. A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram[J]. Mechanical Systems and Signal Processing, 2011, 25(1): 431-451.
[9] Wang Dong,Tse P W,Kwok L T. An enhanced Kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2013, 35(1-2): 176-199
[10] Antoni J. The Infogram: Entropic evidence of the signature of repetitive transients[J]. Mechanical Systems and Signal Processing, 2016, 74(1): 73-94.
[11] Wang Dong. An extension of the infograms to novel Bayesian inference for bearing fault feature identification[J]. Mechanical Systems and Signal Processing , 2016, 80(1): 19-30.
[12] Dragomiretskiy K,Zosso D. Variational mode decomposition. IEEE Transaction on Signal Processing,2014,62(3): 531-544.
[13] Kedadouche M,Thomas M,Tahan A. A comparative study between Empirical Wavelet Transforms and Empirical Mode Decomposition methods: Application to bearing defect diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 81(15): 88-107.
[14] 马增强,李亚超,刘政,等. 基于变分模态分解和Teager能量算子的滚动轴承故障特征提取[J]. 振动与冲击,2016,35(13):134-139.
MA Zeng-qiang,LI Ya-chao,LIU Zheng, et. al. Rolling bearing’ fault feature extraction based on variational mode decomposition and Teager energy operator[J]. Journal of vibration and shock, 2016, 35(13): 134-139.
[15] 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(2): 100-131.
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