An improved Kurtogram based on band-pass envelope spectral Kurtosis with its application in bearing fault diagnosis

ZHANG Long1,2,3, MAO Zhide2, YANG Shixi1, LI Xinglin3

Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (23) : 171-179.

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PDF(3796 KB)
Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (23) : 171-179.

An improved Kurtogram based on band-pass envelope spectral Kurtosis with its application in bearing fault diagnosis

  • ZHANG Long1,2,3,   MAO Zhide2,  YANG Shixi1,  LI Xinglin3
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Abstract

Kurtogram is an effective approach to determine the band-pass filter’s central frequency and band width parameters in the resonant demodulation method applied in rolling bearing fault diagnosis.In Kurtogram,a time domain signal’s Kurtosis value was taken as the filtering effect measure index,but this index was easy to be affected by non-Gaussian noise and occasional non-periodic impact,and cause wrong selection of filtering frequency band.Here,considering feature difference between the envelope spectrum of occasional impact and non-Gaussian noise and that of periodic impact,removing the influence of impacts caused by gear local fault and rotor rubbing,the middle segment of the filtered signal’s envelope spectrum was intercepted according to a certain rule,the Kurtosis of this segment envelope spectrum was proposed to measure the strength of periodic impact and called the band-pass envelope spectral kurtosis.The band-pass envelope spectral Kurtosis was employed to replace the filtered time domain signal’s Kurtosis and obtain an improved Kurtogram method.The effectiveness and advantages of the proposed method were verified with analyses of simulation signals and actual measured signals.

Key words

resonant demodulation / spectral kurtosis / fault diagnosis / feature extraction

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ZHANG Long1,2,3, MAO Zhide2, YANG Shixi1, LI Xinglin3. An improved Kurtogram based on band-pass envelope spectral Kurtosis with its application in bearing fault diagnosis[J]. Journal of Vibration and Shock, 2018, 37(23): 171-179

References

[1] 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(01): 35-37.
WANG Hong-chao,CHEN Jin,DONG Guang-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(01): 35-37.
 [2] Dwyer R. Detection of non-Gaussian signals by frequency domain Kurtosis estimation[C]. Boston, MA, USA: 1983.
 [3] Antoni J. The spectral kurtosis: a useful tool for characterising non-stationary 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] Zhang Y, Randall R B. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram[J]. Mechanical Systems and Signal Processing,2009,23(5): 1509-1517.
 [7] 代士超,郭瑜,伍星,等. 基于子频带谱峭度平均的快速谱峭度图算法改进[J]. 振动与冲击,2015,34(07): 98-102.
DAI Shi-chao,GUO Yu,WU Xing, et al. Improvement on fast kurtogram algorithm basedon sub-frequency-band spectral kurtosis average[J]. Journal of Vibration and Shock, 2015,34(07): 98-102.
 [8] Endo H, Randall R B. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter[J]. Mechanical Systems and Signal Processing,2007,21(2): 906-919.
 [9] Combet F, Gelman L. Optimal filtering of gear signals for early damage detection based on the spectral kurtosis[J]. Mechanical Systems and Signal Processing, 2009,23(3): 652-668.
[10] Barszcz T, Jabłoński 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.
[11] Wang D, Tse P W, Tsui K L. An enhanced Kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing,2013,35(1–2): 176-199.
[12] Tse P W, Wang D. The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2”[J]. Mechanical Systems and Signal Processing,2013, 40(2): 499-519.
[13] Zhang X, Kang J, Zhao J, et al. Rolling element bearings fault diagnosis based on correlated kurtosis kurtogram.[J]. Journal of Vibroengineering,2015,17(6).3023-3034
[14] 唐贵基,王晓龙. 自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J]. 中国电机工程学报,2015,35(6): 1436-1444.
TANG GuIji,WANGXiaolong. Adaptive Maximum Correlated Kurtosis Deconvolution Method and Its Application onIncipient Fault Diagnosis of Bearing[J].Proceedings of the CSEE,2015,35(6): 1436-1444.
[15] Mcfadden P D, Smith J D. Vibration monitoring of rolling element bearings by the high-frequency resonance technique-a review[J]. Tribology International,1984,17(1): 3-10.
[16] Heyns T, Godsill S J, de Villiers J P, et al. Statistical gear health analysis which is robust to fluctuating loads and operating speeds[J]. Mechanical Systems and Signal Processing, 2012,27(1): 651-666.
[17] He W, Jiang Z, Feng K. Bearing fault detection based on optimal wavelet filter and sparse code shrinkage[J]. Measurement,2009,289(6): 1066-1090.
[18] Qiu H, Lee J, Lin J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration,2006,289(1): 1066-1090.
 
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