A Novel Bearing Fault Diagnosis Method Based on LMD and Wavelet Packet Energy Entropy

References

[1] Zhang C, Chen JJ, Xu YL. A bearing fault diagnosis method based on EMD and difference spectrum theory of singular value. J Vibration Eng. 2011;24(5):539–45.Search in Google Scholar

[2] Chu FL, Peng ZK, Feng ZP. Modern signal processing methods in mechanical fault diagnosis. China: Beijing Science Press, 2009Search in Google Scholar

[3] Strangas EG, Aviyente S, Zaidi SSH. Time-frequency analysis for efficient fault diagnosis and failure prognosis for interior permanent-magnet AC motors. Ind Electron IEEE Trans. 2008;55(12):4191–99.10.1109/TIE.2008.2007529Search in Google Scholar

[4] Zhang HY. A new method for analyzing non-stationary signal-local wave analysis. J Electronics Inf Technol. 2003;1(1):1327–33.Search in Google Scholar

[5] Forrester BD. Analysis of gear vibration in the time-frequency domain. Proceeding of the 44th Meeting of the Mechanical Failures Prevention Group of the Vibration Institute, Virginia Beach VA, 3–5 April, 1989.Search in Google Scholar

[6] Zhao HM, Deng W, Yang XH, Li XM. Research on a vibration signal analysis method for motor bearing. Optik. 2016;127(20):10014–23.10.1016/j.ijleo.2016.07.046Search in Google Scholar

[7] Žvokelj M, Zupan S, Prebil I. EEMD-based multi-scale ICA method for slewing bearing fault detection and diagnosis. J Sound Vib. 2016;370:394–423.10.1016/j.jsv.2016.01.046Search in Google Scholar

[8] Dragomiretskiy K, Zosso D. Vibrational mode decomposition. IEEE Trans Process. 2014;62(3):531–44.10.1109/TSP.2013.2288675Search in Google Scholar

[9] Li ZP, Chen JL, Zi YY, Pan J. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive. Mech Syst Signal Process. 2017;85:512–29.10.1016/j.ymssp.2016.08.042Search in Google Scholar

[10] Zhang W, Li CH, Peng GL, Chen YH, Zhang ZJ. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process. 2018;100:439–53.10.1016/j.ymssp.2017.06.022Search in Google Scholar

[11] Yu Y, Yu DJ, Cheng JS. A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J Vib Stock. 2006;294(1–2):269–77.10.1016/j.jsv.2005.11.002Search in Google Scholar

[12] Yu K, Lin TR, Tan JW. A bearing fault diagnosis technique based on singular values of EEMD spatial condition matrix and Gath-Gevaclustering. Appl Acoust. 2017;121:33–45.10.1016/j.apacoust.2017.01.023Search in Google Scholar

[13] Lei Y, He Z, Zi Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech Syst Signal Process. 2009;23(4):1327–38.10.1016/j.ymssp.2008.11.005Search in Google Scholar

[14] Guo W, Tse PW. A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals. J Vib Stock. 2013;332(2):423–41.10.1016/j.jsv.2012.08.017Search in Google Scholar

[15] Gilles J. Empirical wavelet transform. IEEE T Signal Process. 2013;61(16):3999–4010.10.1109/TSP.2013.2265222Search in Google Scholar

[16] Cao HR, Fan F, Zhou K, He ZJ. Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement. 2016;82:439–49.10.1016/j.measurement.2016.01.023Search in Google Scholar

[17] Pan J, Chen JL, Zi YY, et al. Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment. Mech Syst Signal Process. 2016;72–73:160–83.10.1016/j.ymssp.2015.10.017Search in Google Scholar

[18] Zhao XZ, Ye BY. Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock. Mech Syst Signal Process. 2011;25(5):1617–31.10.1016/j.ymssp.2011.01.003Search in Google Scholar

[19] Wang JG, Li J, Liu YY. An improved method for determining effective order rank of SVD denoising. J Vib Stock. 2014;33(12):176–80.Search in Google Scholar

[20] Wang C, Kong FR, Huang WG, et al. Application of improved singular value decomposition in bearing fault diagnosis. J Vib Eng. 2014;27(2):296–303.Search in Google Scholar

[21] 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

[22] Wang Y, Xu G, Liang L, et al. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mech Syst Signal Process. 2015;54:259–76.10.1016/j.ymssp.2014.09.002Search in Google Scholar

[23] Zhao LY, Wang L, Yan RQ. Rolling bearing fault diagnosis based on wavelet packet decomposition and multi-scale permutation entropy. Entropy. 2015;17(9):6447–61.10.3390/e17096447Search in Google Scholar

[24] 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

[25] Wang Y, He Z, Zi Y. A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis. Meas Sci Technol. 2009;20(2):025704.10.1088/0957-0233/20/2/025704Search in Google Scholar

[26] Cheng J, Zhang K, Yang Y. An order tracking technique for the gear fault diagnosis using local mean decomposition method. Mech Mach Theory. 2012;55:67–76.10.1016/j.mechmachtheory.2012.04.008Search in Google Scholar

[27] Li YB, Xu MQ, Wang RX, Huang WH. A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multi-scale fuzzy entropy. J Sound Vib. 2016;360:277–99.10.1016/j.jsv.2015.09.016Search in Google Scholar

[28] Liu ZL, Zuo MJ, Jin YQ, Pan D, Qin Y. Improved local mean decomposition for modulation information mining and its application to machinery fault diagnosis. J Sound Vib. 2017;397:266–81.10.1016/j.jsv.2017.02.055Search in Google Scholar

[29] Lei YG, Lin J, He ZJ, Zi YY. Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mech Syst Signal Process. 2011;25:1738–49.10.1016/j.ymssp.2010.12.011Search in Google Scholar

[30] Zhao LM, Zhu XJ. Feature extraction and classification of electroencephalogram signals based on local mean decomposition and sample entropy. Comput Eng. 2017;43(2):299–303.Search in Google Scholar

[31] Liu XL, Liu HG, Yang JH, Litak G, Cheng G, Han S. Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system. Mech Syst Signal Process. 2017;96:58–76.10.1016/j.ymssp.2017.04.006Search in Google Scholar

[32] Mei HB. Vibration monitoring and diagnosis of rolling bearing. Beijing: China Machine press, 1996.Search in Google Scholar

[33] Han MH, Pan JL. A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings. Measurement. 2015;76:7–19.10.1016/j.measurement.2015.08.019Search in Google Scholar

[34] 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

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

[36] 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

[37] Ding YL, Li AQ, Miao CQ. Investigation on the structural damage alarming method based on wavelet packet energy spectrum. Eng Mech. 2006;23(8):42–48Search in Google Scholar

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

[39] Han J, Zhang RL. Fault mechanism and diagnosis technology of rotating machinery. Beijing: China Machine Press, 1997:170.Search in Google Scholar

[40] Diana G, Fossati F, Resta F. High speed railway collecting pantographs active control and overhead lines diagnosis solution. Vehicle Syst Dyn. 1998;30:69–85.10.1080/00423119808969436Search in Google Scholar

[41] Ding YL, Li AQ, Miao CQ. Theoretical research on structural damage alarming of long-span bridges using wavelet packet analysis. J Southeast Univ. 2005;21(4):459–62.Search in Google Scholar

[42] 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;9(1):14.10.3390/e19010014Search in Google Scholar

[43] Norden EH, Zheng S, Steven RL, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Royal Soc London A. 1998;454:903–95.10.1098/rspa.1998.0193Search in Google Scholar

[44] Case Western Reserve University Bearing Data Center Website. 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