IEEE/CAA Journal of Automatica Sinica
Citation: | C. Sun, H. P. Yin, Y. X. Li, and Y. Chai, "A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1188-1198, Jun. 2021. doi: 10.1109/JAS.2020.1003438 |
[1] |
J. Wang, Y. Peng, and W. Qiao, “Current-aided order tracking of vibration signals for bearing fault diagnosis of direct-drive wind turbines,” IEEE Trans. Industrial Electronics, vol. 63, no. 10, pp. 6336–6346, 2016. doi: 10.1109/TIE.2016.2571258
|
[2] |
J. Wang, L. Qiao, Y. Ye, and Y. Chen, “Fractional envelope analysis for rolling element bearing weak fault feature extraction,” IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 2, pp. 353–360, 2016.
|
[3] |
Z. Zhao, S. Wu, B. Qiao, S. Wang, and X. Chen, “Enhanced sparse period-group lasso for bearing fault diagnosis,” IEEE Trans. Industrial Electronics, vol. 66, no. 3, pp. 2143–2153, 2019. doi: 10.1109/TIE.2018.2838070
|
[4] |
Y. Qin, “A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis,” IEEE Trans. Industrial Electronics, vol. 65, no. 3, pp. 2716–2726, 2018. doi: 10.1109/TIE.2017.2736510
|
[5] |
B. Qiao, X. Zhang, J. Gao, R. Liu, and X. Chen, “Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction,” Mechanical Systems and Signal Processing, vol. 83, pp. 93–115, 2017. doi: 10.1016/j.ymssp.2016.05.046
|
[6] |
W. He, Y. Ding, Y. Zi, and I. W. Selesnick, “Repetitive transients extraction algorithm for detecting bearing faults,” Mechanical Systems and Signal Processing, vol. 84, pp. 227–244, 2017. doi: 10.1016/j.ymssp.2016.06.035
|
[7] |
Y. Ding, W. He, B. Chen, Y. Zi, and I. W. Selesnick, “Detection of faults in rotating machinery using periodic time-frequency sparsity,” Journal of Sound and Vibration, vol. 382, pp. 357–378, 2016. doi: 10.1016/j.jsv.2016.07.004
|
[8] |
I. W. Selesnick, “Resonance-based signal decomposition: A new sparsity-enabled signal analysis method,” Signal Processing, vol. 91, no. 12, pp. 2793–2809, 2011. doi: 10.1016/j.sigpro.2010.10.018
|
[9] |
G. Cai, X. Chen, and Z. He, “Sparsity-enabled signal decomposition using tunable q-factor wavelet transform for fault feature extraction of gearbox,” Mechanical Systems and Signal Processing, vol. 41, no. 1–2, pp. 34–53, 2013. doi: 10.1016/j.ymssp.2013.06.035
|
[10] |
B. Yang, R. Liu, and X. Chen, “Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD,” IEEE Trans. Industrial Informatics, vol. 13, no. 3, pp. 1321–1331, 2017. doi: 10.1109/TII.2017.2662215
|
[11] |
M. Elad, Sparse and Redundant Representations – From Theory to Applications in Signal and Image Processing. Heidelberg, Germany: Springer Publishing Company, 2010.
|
[12] |
M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Processing, vol. 15, no. 12, pp. 3736–3745, 2006. doi: 10.1109/TIP.2006.881969
|
[13] |
S. Liu, Y. Xian, H. Li, and Z. Yu, “Text detection in natural scene images using morphological component analysis and laplacian dictionary,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 1, pp. 214–222, 2020.
|
[14] |
M. Aharon, M. Elad, A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. on Signal Proc., vol. 54, no. 11, Article No. 4311, 4322. Nov. 2006. doi: 10.1109/TSP.2006.881199
|
[15] |
W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 457–464, IEEE, 2011.
|
[16] |
K. Zhu and B. Vogel-Heuser, “Sparse representation and its applications in micro-milling condition monitoring: noise separation and tool condition monitoring,” The Int. Journal of Advanced Manufacturing Technology, vol. 70, no. 1–4, pp. 185–199, 2014. doi: 10.1007/s00170-013-5258-5
|
[17] |
Z. Feng and M. Liang, “Complex signal analysis for planetary gearbox fault diagnosis via shift invariant dictionary learning,” Measurement, vol. 90, pp. 382–395, 2016. doi: 10.1016/j.measurement.2016.04.078
|
[18] |
H. Jiang, J. Chen, G. Dong, T. Liu, and G. Chen, “Study on hankel matrix-based svd and its application in rolling element bearing fault diagnosis,” Mechanical Systems and Signal Processing, vol. 52, pp. 338–359, 2015.
|
[19] |
S. Chen, Y. Yang, K. Wei, X. Dong, Z. Peng, and W. Zhang, “Timevarying frequency-modulated component extraction based on parameterized demodulation and singular value decomposition,” IEEE Trans. Instrumentation and Measurement, vol. 65, no. 2, pp. 276–285, 2016. doi: 10.1109/TIM.2015.2494632
|
[20] |
T. Goldstein and S. Osher, “The split bregman method for l1-regularized problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 323–343, 2009. doi: 10.1137/080725891
|
[21] |
J. R. Stack, T. G. Habetler, and R. G. Harley, “Fault classification and fault signature production for rolling element bearings in electric machines,” IEEE Trans. Industry Applications, vol. 40, no. 3, pp. 735–739, 2004. doi: 10.1109/TIA.2004.827454
|
[22] |
W. Zhou, B. Lu, T. G. Habetler, and R. G. Harley, “Incipient bearing fault detection via motor stator current noise cancellation using wiener filter,” IEEE Trans. Industry Applications, vol. 45, no. 4, pp. 1309–1317, 2009. doi: 10.1109/TIA.2009.2023566
|
[23] |
H. Yang, H. Lin, and K. Ding, “Sliding window denoising k-singular value decomposition and its application on rolling bearing impact fault diagnosis,” Journal of Sound and Vibration, vol. 421, pp. 205–219, 2018. doi: 10.1016/j.jsv.2018.01.051
|
[24] |
V. C. Leite, J. G. B. da Silva, G. F. C. Veloso, L. E. B. da Silva, G. Lambert-Torres, E. L. Bonaldi, and L. E. d. L. de Oliveira, “Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current,” IEEE Trans. Industrial Electronics, vol. 62, no. 3, pp. 1855–1865, 2015. doi: 10.1109/TIE.2014.2345330
|
[25] |
T. Igarashi, “Noise of ball bearing: 2nd report, case of fitted ball bearing,” Bulletin of JSME, vol. 5, no. 17, pp. 184–194, 1962. doi: 10.1299/jsme1958.5.184
|
[26] |
R. Yang and Z. Su, “Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation,” Bioinformatics, vol. 26, no. 12, pp. i168–i174, 2010. doi: 10.1093/bioinformatics/btq189
|
[27] |
J. Long, H. Wang, P. Li, and H. Fan, “Applications of fractional lower order time-frequency representation to machine bearing fault diagnosis,” IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 4, pp. 734–750, 2017. doi: 10.1109/JAS.2016.7510190
|
[28] |
N. Sawalhi, R. Randall, and H. Endo, “The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2616–2633, 2007. doi: 10.1016/j.ymssp.2006.12.002
|
[29] |
T. Bengtsson and J. E. Cavanaugh, “An improved akaike information criterion for state-space model selection,” Computational Statistics &Data Analysis, vol. 50, no. 10, pp. 2635–2654, 2006.
|
[30] |
P. Kruczek, A. Wyłomańska, M. Teuerle, and J. Gajda, “The modified yule-walker method for α-stable time series models,” Physica A: Statistical Mechanics and Its Applications, vol. 469, pp. 588–603, 2017.
|
[31] |
Y. Cheng, N. Zhou, W. Zhang, and Z. Wang, “Application of an improved minimum entropy deconvolution method for railway rolling element bearing fault diagnosis,” Journal of Sound and Vibration, vol. 425, pp. 53–69, 2018. doi: 10.1016/j.jsv.2018.01.023
|
[32] |
R. A. Wiggins, “Minimum entropy deconvolution,” Geoexploration, vol. 16, no. 1–2, pp. 21–35, 1978. doi: 10.1016/0016-7142(78)90005-4
|
[33] |
K. Engan, S. O. Aase, and J. H. Husoy, “Method of optimal directions for frame design,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Proc., IEEE, Arizona, USA, pp. 2443–2446, 1999.
|
[34] |
H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Statistical Society:Series B (Statistical Methodology)
|
[35] |
J. Wang, L. Zhao, J. Liu, and N. Kato, “Smart resource allocation for mobile edge computing: A deep reinforcement learning approach,” IEEE Trans. on Emerging Topics in Computing, Mar. 2019. DOI: 10.1109/TETC2019.2902661.
|
[36] |
S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” Multiscale Modeling &Simulation, vol. 4, no. 2, pp. 460–489, 2005.
|
[37] |
Case Western Reserve University Bearing Data Center Website, Mar. 2015. [online] Available: http://csegroups.case.edu/bearingdatacenter/pages/download-data-file/
|
[38] |
W. Sun, G. An Yang, Q. Chen, A. Palazoglu, and K. Feng, “Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation,” Journal of Vibration and Control, vol. 19, no. 6, pp. 924–941, 2013. doi: 10.1177/1077546311435348
|
[39] |
C. Wang, M. Gan, and C. Zhu, “Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory,” Journal of Intelligent Manufacturing, vol. 29, no. 4, pp. 937–951, 2018. doi: 10.1007/s10845-015-1153-2
|