Study on a Novel Bearing Fault Diagnosis Method from Frequency and Energy Perspective

References

[1] Tiwari R, Gupta VK, Kankar PK. Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. J Vibration Control. 2015;21(3):461–67.10.1177/1077546313490778Search in Google Scholar

[2] Kankar PK, Sharma SC, Harsha SP. Fault diagnosis of high speed rolling element bearings due to localized defects using response surface method. J Dyn Syst Meas Control. 2011;133(3):031007.10.1115/1.4003371Search in Google Scholar

[3] Du W, Tao J, Li Y, Liu C. Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mech Syst Signal Process. 2014;43:57–75.10.1016/j.ymssp.2013.09.003Search in Google Scholar

[4] Zhao HM, Li DY, Deng W, Yang XH. Research on vibration suppression method of alternating current motor based on fractional order control strategy. P I Mech Eng E-J Pro. 2017;231(3):786–99.10.1177/0954408916637380Search in Google Scholar

[5] Peng ZK, Chu FL. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process. 2004;18:199–221.10.1016/S0888-3270(03)00075-XSearch in Google Scholar

[6] Lei Y, Lin J, He Z, Zuo MJ. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process. 2013;35:108–26.10.1016/j.ymssp.2012.09.015Search in Google Scholar

[7] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng QN, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond. 1998;454(1971):903–95.10.1098/rspa.1998.0193Search in Google Scholar

[8] Smith JS. The local mean decomposition and its application to EEG perception data. J Royal Soc Interface. 2005;2(5):443–54.10.1098/rsif.2005.0058Search in Google Scholar PubMed PubMed Central

[9] Antoni J, Randall RB. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Signal Process. 2006;20:308–31.10.1016/j.ymssp.2004.09.002Search in Google Scholar

[10] Wang YX, Liang M. Identification of multiple transient faults based on the adaptive spectral kurtosis method. J Sound Vib. 2012;331:470–86.10.1016/j.jsv.2011.08.029Search in Google Scholar

[11] Sawalhi N, Randall RB, Endo H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech Syst Signal Process. 2007;21:2616–33.10.1016/j.ymssp.2006.12.002Search in Google Scholar

[12] Zhao HM, Sun M, Deng W, Yang XH. A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy. 2017;19(1):14.10.3390/e19010014Search in Google Scholar

[13] Jia X, Zhao M, Di Y, Jin C, Lee J. Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement. J Sound Vib. 2017;386:433–48.10.1016/j.jsv.2016.10.005Search in Google Scholar

[14] Tan J, Chen X, Wang J, Chen H, Cao H, Zi Y, et al. Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis. Mech Syst Signal Process. 2009;23:811–22.10.1016/j.ymssp.2008.07.011Search in Google Scholar

[15] He Q, Wang J. Effects of multiscale noise tuning on stochastic resonance for weak signal detection. Digit Signal Process. 2012;22:614–21.10.1016/j.dsp.2012.02.008Search in Google Scholar

[16] Lei Y, Han D, Lin J, He Z. Planetary gearbox fault diagnosis using an adaptive stochastic resonance method. Mech Syst Signal Process. 2013;38:113–24.10.1016/j.ymssp.2012.06.021Search in Google Scholar

[17] Banerjee M, Pal NR. Feature selection with SVD entropy: some modification and extension. Inform Sci. 2014;264:118–34.10.1016/j.ins.2013.12.029Search in Google Scholar

[18] Golafshan R, Yuce Sanliturk K. SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults. Mech Syst Signal Process. 2016;70–71:36–50.10.1016/j.ymssp.2015.08.012Search in Google Scholar

[19] Ovaska SJ, Vanlandingham HF, Kamiya A. Fusion of soft computing and hard computing in industrial applications an overview. IEEE Trans Syst, Man, Cybernetics Part C. 2002;32(2):72–79.10.1109/TSMCC.2002.801354Search in Google Scholar

[20] Li XM, Deng W, Zhao HM, Zheng GH. Study on a novel fault diagnosis method of rolling bearing in motor. Recent Patents Mech Eng. 2016;9(1):144–52.10.2174/2212797609666160408154213Search in Google Scholar

[21] Deng W, Zhao HM, Zou L, Li GY, Yang XH, Wu DQ. A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput. 2017;21(15):4387–98.10.1007/s00500-016-2071-8Search in Google Scholar

[22] De Lathauwer L, Castaing J. Tensor-based techniques for the blind separation of DS-CDMA signal[J]. IEEE Trans Process. 2007;87(32):322–36.10.1016/j.sigpro.2005.12.015Search in Google Scholar

[23] Li XM, Li W, Sun YN, Zheng GH. Blind source separation of vibration signal of electric motor velocity modulation system. Lecture Notes Electr Eng. 2011;88:487–95.10.1007/978-3-642-19959-2_59Search in Google Scholar

[24] Brown GJ, Wang DL. Separation of speech by computational auditory scene analysis. Signals and communication technology: speech enhancement. Berlin: Springer-Verlag, 2005: 371–402.Search in Google Scholar

[25] Naceur MS, Loghmari MA, Boussem MR. The contribution of the source separation method in the decomposition of mixed pixels. IEEE Trans Geo-Sci Remote Sensing. 2004;42(11):2642–53.10.1109/TGRS.2004.834764Search in Google Scholar

[26] Nuzillard D, Bijaoui A. Blind source separation and analysis of multi-spectral astronomical images. Astron Astrophys Suppl. 2000;147:129–38.10.1051/aas:2000292Search in Google Scholar

[27] Kolba J, Jouny I. Blind source separation in tumor detection in mammograms. In: Proceedings of the IEEE 32nd Annual Northeast Bioengineering Conference. Piscataway: IEEE, 2006: 65–66.10.1109/NEBC.2006.1629754Search in Google Scholar

[28] Ye Y, Zhang ZL, Zeng J, Peng L. A fast and adaptive ICA algorithm with its application to fatal electrocardiogram extraction. Appl Math Comput. 2008;205(2):799–806.Search in Google Scholar

[29] Guo X, Chang C, Lam EY. Blind separation of electron paramagnetic resonance signals using diversity minimization. J Magn Reson. 2010;204(1):26–36.10.1016/j.jmr.2010.01.014Search in Google Scholar PubMed

[30] Sun Y, Ridge C, Rio FD, Shaka AJ, Xin J. Post-processing and sparse blind source separation of positive and partially overlapped data. Signal Process. 2011;91(8):1838–51.10.1016/j.sigpro.2011.02.007Search in Google Scholar

[31] Belouchrani A, Meraim KA, Cardoso LF, Moulines E. Second-order blind separation of correlated sources. In: Processing of the International Conference on Digital Signal Processing, Cyprus, 1993: 346–51.Search in Google Scholar

[32] Belouchrani A, Meraim KA, Cardoso JF, Moulines E. A blind source separation technique using second-order statistics. IEEE Tranq Signal Proc. 1997;45(2):434–44.10.1109/78.554307Search in Google Scholar

[33] Staszweski WJ. Wavelet based compression and feature selection for vibration analysis. J Sound Vibr. 1998;211(5):735–60.10.1006/jsvi.1997.1380Search in Google Scholar

[34] Lou X, Kenneth KA, Loparo A. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech Syst Signal Process. 2004;18(5):1077–95.10.1016/S0888-3270(03)00077-3Search in Google Scholar

[35] Wang Y, Liang DK, Zhou B. Damage diagnosis for optical fiber grating smart structure based on wavelet packet analysis. Opt Precision Eng. 2007;15(11):1731–37.Search in Google Scholar

[36] Hemmati F, Orfali W, Gadala MS. Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Appl Acoust. 2016;104:101–18.10.1016/j.apacoust.2015.11.003Search in Google Scholar

[37] Zeng XW, Zhao WM, Sheng JQ. Corresponding relationships between nodes of decomposition tree of wavelet packet and frequency bands of signal subspace. Acta Seismol Sinica. 2008;30(1):90–96.10.1007/s11589-008-0091-xSearch in Google Scholar

[38] Bin GF, Gao JJ, Li XJ, Dhillon BS. Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mech Syst Signal Process. 2012;27:696–711.10.1016/j.ymssp.2011.08.002Search in Google Scholar

[39] Kyprianou A, Lewin PL, Efthimiou V, Stavrou A, Georghiou GE. Wavelet packet denoising for online partial discharge detection in cables and itsapplication to experimental field results. Measure Sci Technol. 2006;17(9):2367–79.10.1088/0957-0233/17/9/001Search in Google Scholar

[40] Lin J, Qu LS. Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. Sound Vibr. 2000;234(1):135–48.10.1006/jsvi.2000.2864Search in Google Scholar

[41] Zhao HM, Deng W, Yang XH, Li XM, Li ZG. Study on a novel fault diagnosis method based on information fusion method. J Vibroeng. 2016;18(8):4885–5631.10.21595/jve.2016.16859Search in Google Scholar

[42] Li AQ, Ding YL. Early warning theory and its application of engineering structure damage. China: Beijing Science Press, 2007.Search in Google Scholar

[43] Yu ZW, Su BK, Zeng M. Application of wavelet packet in fault diagnosis system of large scale DC motor rotor. Proc CSEE. 2005;25(22):158–62.Search in Google Scholar

[44] Case Western Reserve University Bearing Data Center. Available at http://csegroups.case.edu/ bearingdatacenter/home. Accessed: 12 Jan 2016.Search in Google Scholar

[45] Smith WA, Randall RB. Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech Syst Signal Process. 2015;64–65:100–31.10.1016/j.ymssp.2015.04.021Search in Google Scholar

[46] Yu F. The application of wavelet analysis to fault diagnosis of rotating machinery. Xidian University in Candidacy for the Degree of Doctor of Philosophy, 1998:12.Search in Google Scholar