Repetitive transients are a key symptom for the occurrence of incipient fault of rolling element bearings. Therefore, an optimal wavelet filtering method is developed by maximizing the L-kurtosis through the genetic algorithm to extract the weak repetitive transients buried in the heavy noise and disturbed by the outliers. First, the capability of L-kurtosis for characterizing the impulsiveness and cyclostationary of repetitive transients is numerically studied at different degrees of noise. Then, the center frequency and band width of morlet wave filter are adaptively determined by the genetic algorithm and the maximization of L-kurtosis. Finally, both simulation and experiments are performed to validate the efficacy of the proposed method. Results show that the proposed method is more powerful and reliable than the other commonly used indexes-based optimal wavelet filtering methods.
AntoniJ (2007) Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing21(1): 108–124.
2.
AntoniJ (2016) The infogram: entropic evidence of the signature of repetitive transients. Mechanical Systems and Signal Processing74: 73–94.
3.
BaoWTuXHuY, et al. (2020) Envelope spectrum L-kurtosis and its application for fault detection of rolling element bearings. IEEE Transactions on Instrumentation and Measurement69(5): 1993–2002.
4.
BarszczTJabŁońskiA (2011) A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mechanical Systems and Signal Processing25(1): 431–451.
5.
BozchalooiISLiangM (2007) A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection. Journal of Sound and Vibration308: 246–267.
6.
ChenBPengFYWangHY, et al. (2020) Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction. Mechanism and Machine Theory154: 104051.
7.
EricssonSGripNJohanssonE, et al. (2005) Towards automatic detection of local bearing defects in rotating machines. Mechanical Systems and Signal Processing19: 509–535.
8.
GaoQXiangJWHouSM, et al. (2021) Method using L-kurtosis and enhanced clustering-based segmentation to detect faults in axial piston pumps. Mechanical Systems and Signal Processing147: 107130.
9.
GuXHYangSPLiuYQ, et al. (2018) Compound faults detection of the rolling element bearing based on the optimal complex Morlet wavelet filter. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science232(10): 1786–1801.
10.
HemmatiFOrfaliWGadalaMS (2016) Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Applied Acoustics104: 101–118.
11.
HoskingJRM (1990) L-Moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society: Series B (Methodological)52(1): 105–124.
12.
LiHLiuTWuX, et al. (2020) Enhanced frequency band entropy method for fault feature extraction of rolling element bearings. IEEE Transactions on Industrial Informatics16(9): 5780–5791.
13.
LiuSPHouSMHeKD, et al. (2018) L-Kurtosis and its application for fault detection of rolling element bearings. Measurement116: 523–532.
14.
MahgounHChaariFFelkaouiA, et al. (2019) L-kurtosis and improved complete ensemble EMD in early fault detection under variable load and speed. In: FakhfakhTKarraCBouazizS, et al. (eds), Advances in Acoustics and Vibration II. ICAV 2018. Applied Condition Monitoring. Cham: Springer, Vol 13.
15.
McFaddenPDSmithJD (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique-a review. Tribology International17: 3–10.
16.
PeterWTWangD (2013) The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement–Parts 1 and 2”. Mech. Syst. Signal. Process40: 499–519.
17.
QiuHLeeJLinJ, et al. (2006) Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration289: 1066–1090.
18.
SinghJDarpeAKSinghSP (2018) Rolling element bearing fault diagnosis based on over-complete rational dilation wavelet transform and auto-correlation of analytic energy operator. Mechanical Systems and Signal Processing100: 662–693.
19.
TsePWWangD (2013) The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection: Part 2 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”. Mechanical Systems and Signal Processing40(2): 520–544.
20.
WangD (2018a) Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients. Mechanical Systems and Signal Processing108: 360–368.
21.
WangD (2018b) Spectral L2/L1 norm: a new perspective for spectral kurtosis for characterizing non-stationary signals. Mechanical Systems and Signal Processing104: 290–293.
22.
WangDMiaoQ (2015) Smoothness index-guided Bayesian inference for determining joint posterior probability distributions of anti-symmetric real Laplace wavelet parameters for identification of different bearing faults. Journal of Sound and Vibration345: 250–266.
23.
YanXALiuYJiaMP (2019) Research on an enhanced scale morphological-hat product filtering in incipient fault detection of rolling element bearings. Measurement147: 106856.
24.
YangRLiHKWangCG, et al. (2019) Rolling element bearing weak feature extraction based on improved optimal frequency band determination. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science233(2): 623–634.
