A review on empirical mode decomposition in fault diagnosis of rotating machinery

Mechanical Systems and Signal Processing - Tập 35 Số 1-2 - Trang 108-126 - 2013
Yaguo Lei1, Jing Lin1, Zhengjia He1, Ming J. Zuo2
1State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China
2Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada T6G2G8#TAB#

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Tài liệu tham khảo

Fan, 2008, Machine fault feature extraction based on intrinsic mode functions, Meas. Sci. Technol., 19, 1, 10.1088/0957-0233/19/4/045105

Lei, 2008, New clustering algorithm-based fault diagnosis using compensation distance evaluation technique, Mech. Syst. Signal Process., 22, 419, 10.1016/j.ymssp.2007.07.013

Lei, 2011, Application of an improved kurtogram method for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process., 25, 1738, 10.1016/j.ymssp.2010.12.011

Meltzer, 2004, Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps, Mech. Syst. Signal Process., 18, 985, 10.1016/j.ymssp.2004.01.009

Cempel, 2007, Multidimensional condition monitoring of machines in non-stationary operation, Mech. Syst. Signal Process., 21, 1233, 10.1016/j.ymssp.2006.04.001

Bartelmus, 2009, A new feature for monitoring the condition of gearboxes in non-stationary operating conditions, Mech. Syst. Signal Process., 23, 1528, 10.1016/j.ymssp.2009.01.014

Urbanek, 2012, Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions, Measurement, 10.1016/j.measurement.2012.04.006

Huang, 1998, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London, A 454, 903, 10.1098/rspa.1998.0193

Srinivasan, 2007, A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops, Control Eng. Pract., 15, 1135, 10.1016/j.conengprac.2007.01.014

Luo, 2012, Hilbert–Huang transform, Hurst and chaotic analysis based flow regime identification methods for an airlift reactor, Chem. Eng. J., 181–182, 570, 10.1016/j.cej.2011.11.093

Xu, 2007, EMD-and SVM-based temperature drift modeling and compensation for a dynamically tuned gyroscope (DTG), Mech. Syst. Signal Process., 21, 3182, 10.1016/j.ymssp.2007.05.006

Guo, 2012, Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model, Renewable E., 37, 241, 10.1016/j.renene.2011.06.023

Tang, 2011, Vibration analysis based on empirical mode decomposition and partial least square, Procedia Eng., 16, 646, 10.1016/j.proeng.2011.08.1136

Zhang, 2010, A new approach to analysis of surface topography, Precis. Eng., 34, 807, 10.1016/j.precisioneng.2010.05.002

Sonia, 2007, Crackle sounds analysis by empirical mode decomposition, IEEE Eng. Med. Biol. Mag., 26, 40, 10.1109/MEMB.2007.289120

E. Ambikairajah, Emerging features for speaker recognition, Proceedings of the Sixth International Conference on Information, Communications & Signal Processing, Singapore, December 10–13, 2007, 1–7.

Yang, 2009, Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique, J. Sound and Vib., 322, 718, 10.1016/j.jsv.2008.11.028

H.Y. Zhang, X.R. Qi, X.L. Sun, et al., Application of Hilbert–Huang transform to extract arrival time of ultrasonic Lamb waves, International Conference on Audio, Language and Image Processing, Shanghai, China, July 7–9, (2008), 1–4.

Huang, 2008, A review on Hilbert–Huang transform: method and its applications to geophysical studies, Rev. Geophys., 46, 1, 10.1029/2007RG000228

Peng, 2004, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mech. Syst. Signal Process., 18, 199, 10.1016/S0888-3270(03)00075-X

Rato, 2008, On the HHT, its problems, and some solutions, Mech. Syst. Signal Process., 22, 1374, 10.1016/j.ymssp.2007.11.028

Chen, 2012, A signal decomposition theorem with Hilbert transform and its application to narrowband time series with closely spaced frequency components, Mech. Syst. Signal Process., 28, 258, 10.1016/j.ymssp.2011.02.002

G. Rilling, P. Flandrin, P. Gonçalvés, On empirical mode decomposition and its algorithms, IEEE–EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP), Grado, Italy, June 8-11-2003.

Tsakalozos, 2011, A formal study of the nonlinearity and consistency of the empirical mode decomposition, Signal Process., 92, 1961, 10.1016/j.sigpro.2011.09.014

Deléchelle, 2005, Empirical mode decomposition: an analytical approach for sifting process, IEEE Signal Process. Lett., 12, 764, 10.1109/LSP.2005.856878

Feldman, 2009, Analytical basics of the EMD: two harmonics decomposition, Mech. Syst. Signal Process., 23, 2059, 10.1016/j.ymssp.2009.04.002

Kerschen, 2008, Toward a fundamental understanding of the Hilbert–Huang transform in nonlinear structural dynamics, J. Vib. Control, 14, 77, 10.1177/1077546307079381

Rilling, 2008, One or two frequencies? The empirical mode decomposition answers, IEEE Trans. Signal Process., 56, 85, 10.1109/TSP.2007.906771

Yang, 2008, A method to eliminate riding waves appearing in the empirical AM/FM demodulation, Digital Signal Process., 18, 488, 10.1016/j.dsp.2007.07.003

He, 2012, Boundary extension for Hilbert–Huang transform inspired by gray prediction model, Signal Process., 92, 685, 10.1016/j.sigpro.2011.09.010

Xun, 2008, A revised Hilbert–Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure, Mech. Syst. Signal Process., 22, 1705, 10.1016/j.ymssp.2008.02.008

Xuan, 2007, EMD sifting based on bandwidth, IEEE Signal Process. Lett., 14, 537, 10.1109/LSP.2007.891833

Li, 2009, Signal feature extraction based on an improved EMD method, Measurement, 42, 796, 10.1016/j.measurement.2009.01.001

Hawley, 2010, Some properties of an empirical mode type signal decomposition algorithm, IEEE Signal Process. Lett., 17, 24, 10.1109/LSP.2009.2030855

Roy, 2011, Raised cosine filter–based empirical mode decomposition, Inst. Eng. Technol., 5, 121

Roy, 2010, Improved signal analysis performance at low sampling rates using raised cosine empirical mode decomposition, Electron. Lett., 46, 176, 10.1049/el.2010.2361

Damerval, 2005, A fast algorithm for bidimensional EMD, IEEE Signal Process. Lett., 12, 701, 10.1109/LSP.2005.855548

Meignen, 2007, A new formulation for empirical mode decomposition based on constrained optimization, IEEE Signal Process. Lett., 14, 932, 10.1109/LSP.2007.904706

Qin, 2006, A new envelope algorithm of Hilbert–Huang Transform, Mech. Syst. Signal Process., 20, 1941, 10.1016/j.ymssp.2005.07.002

Rehman, 2010, Empirical mode decomposition for trivariate signals, IEEE Trans. Signal Process., 58, 1059, 10.1109/TSP.2009.2033730

Fleureau, 2011, Multivariate empirical mode decomposition and application to multichannel filtering, Signal Process., 91, 2783, 10.1016/j.sigpro.2011.01.018

Rilling, 2007, Bivariate empirical mode decomposition, IEEE Signal Process. Lett., 14, 936, 10.1109/LSP.2007.904710

Kopsinis, 2008, Improved EMD using doubly-iterative sifting and high order spline interpolation, EURASIP J. Adv. Signal Process., 2008, 1, 10.1155/2008/128293

Li, 2011, Extraction of time varying information from noisy signals: an approach based on the empirical mode decomposition, Mech. Syst. Signal Process., 25, 812, 10.1016/j.ymssp.2010.10.007

Kopsinis, 2009, Development of EMD-based denoising methods inspired by wavelet thresholding, IEEE Trans. Signal Process., 57, 1351, 10.1109/TSP.2009.2013885

Yang, 2010, An oblique-extrema-based approach for empirical mode decomposition, Digital Signal Process., 20, 699, 10.1016/j.dsp.2009.08.013

X.X. Liu, F.L. Han, J.G. Wang, Wavelet extended EMD noise reduction model for signal trend extraction, Proceedings of the Second International Congress on Image and Signal Processing, Tianjin, China, October 17–19, 2009, 1–5.

Wu, 2009, Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adaptive Data Anal., 1, 1, 10.1142/S1793536909000047

Flandrin, 2004, Empirical mode decomposition as a filter bank, IEEE Signal Process. Lett., 11, 112, 10.1109/LSP.2003.821662

Wu, 2004, A study of the characteristics of white noise using the empirical mode decomposition method, Proc. R. Soc. London, 460A, 1597, 10.1098/rspa.2003.1221

P. Flandrin, P. Gonçalvès, G. Rilling, EMD Equivalent Filter Banks, from Interpretation to Applications. In Hilbert–Huang Transform: introduction and Applications, pp 67–87, World Scientific, Singapore, 360pp.

Lei, 2009, Application of the EEMD method to rotor fault diagnosis of rotating machinery, Mech. Syst. Signal Process., 23, 1327, 10.1016/j.ymssp.2008.11.005

Peng, 2005, A comparison study of improved Hilbert–Huang transform and wavelet transform: application to fault diagnosis for rolling bearing, Mech. Syst. Signal Process., 19, 974, 10.1016/j.ymssp.2004.01.006

Chen, 2006, A B-spline approach for empirical mode decompositions, Adv. Comput. Math., 24, 171, 10.1007/s10444-004-7614-3

D. Xu, Y.C. XU, X. Chen, et al., Life cycle vibration analysis based on EMD of rolling element bearing under ALT by Constant Stress, Proceedings of the Eigth International Conference on Reliability, Maintainability and Safety, Chengdu, China, July 20–24, 2009, 1177–1182.

Cheng, 2007, The application of energy operator demodulation approach based on EMD in machinery fault diagnosis, Mech. Syst. Signal Process., 21, 668, 10.1016/j.ymssp.2005.10.005

Yan, 2006, Hilbert–Huang transform-based vibration signal analysis for machine health monitoring, IEEE Trans. Signal Process., 55, 2320

Li, 2009, Hilbert–Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings, J. Mech. Sci. Technol., 23, 291, 10.1007/s12206-008-1110-5

H. Li, Bearing fault detection based on instantaneous energy spectrum, Proceedings of the Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, China, August 10–12, 2010, 2594–2598.

Chiementin, 2010, Effect of cascade methods on vibration defects detection, J. Vib. Control, 17, 567, 10.1177/1077546310362447

J.M. Mei, Y.H. Liu, Y.K. Xiao, et al., Extraction of Transmission Bearing Fault Characters Based on EMD and Fractal Theory, IEEE Third International Conference on Communication Software and Networks, Xi'an, China, May 27–29, 2011, 215–219.

W.C. Tsao, Y.F. Li, M.C. Pan, Resonant-Frequency band choice for bearing fault diagnosis based on EMD and envelope analysis, Proceedings of the eigth World Congress on Intelligent Control and Automation, Jinan, China, July 6–9, 2010, 1289–1294.

Du, 2006, Improvement of the EMD method and applications in defect diagnosis of ball bearings, Meas. Sci. Technol., 17, 2355, 10.1088/0957-0233/17/8/043

Du, 2007, Application of the EMD method in the vibration analysis of ball bearings, Mech. Syst. Signal Process., 21, 2634, 10.1016/j.ymssp.2007.01.006

Dong, 2009, Sifting process of EMD and its application in rolling element bearing fault diagnosis, J. Mech. Sci. Technol., 23, 2000, 10.1007/s12206-009-0438-9

J. Terrien, C. Marque, B. Karlsson, Automatic detection of mode mixing in empirical mode decomposition using non-stationarity detection: application to selecting IMFs of interest and denoising, EURASIP J. Adv. Signal Process. (2011) 2011, 1–8.

Yan, 2008, Rotary machine health diagnosis based on empirical mode decomposition, J. Vib. Acoust., 130, 021007, 10.1115/1.2827360

Q. Miao, D. Wang, M. Pecht, Rolling element bearing fault feature extraction using EMD-based independent component analysis, IEEE Conference on Prognostics and Health Management, Montreal, Canada, June 20–23, 2011, 1–6.

Yu, 2005, Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings, Mech. Syst. Signal Process., 19, 259, 10.1016/S0888-3270(03)00099-2

H. Li, H.Q. Zheng, Bearing fault detection using envelope spectrum based on EMD and TKEO, Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Shandong, China, October 18–20, 2008, 142–146.

Rai, 2007, Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform, Mech. Syst. Signal Process., 21, 2607, 10.1016/j.ymssp.2006.12.004

Li, 2006, Wigner–Ville distribution based on EMD for faults diagnosis of bearing, Fuzzy Syst. Knowl. Discovery, 4223, 803, 10.1007/11881599_99

F. Chen, X. Zhou, Q.H. Wu, et al., Application of Hilbert–Huang Transformation to fault diagnosis of rotary machinery, Fifth International Symposium on Instrumentation Science and Technology, Shenyang, China, September 15–18, 2008.

Li, 2010, Bearing fault detection and diagnosis based on order tracking and Teager–Huang transform, J. Mech. Sci. Technol., 24, 811, 10.1007/s12206-009-1211-9

Tang, 2012, Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation, Signal Process., 92, 248, 10.1016/j.sigpro.2011.07.013

Yang, 2007, A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM, Measurement, 40, 943, 10.1016/j.measurement.2006.10.010

Cheng, 2006, A fault diagnosis approach for roller bearings based on EMD method and AR model, Mech. Syst. Signal Process., 20, 350, 10.1016/j.ymssp.2004.11.002

Yang, 2006, A roller bearing fault diagnosis method based on EMD energy entropy and ANN, J. Sound Vib., 294, 269, 10.1016/j.jsv.2005.11.002

Cheng, 2009, Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery, Shock Vib., 16, 89, 10.1155/2009/519502

Lei, 2008, A new approach to intelligent fault diagnosis of rotating machinery, Expert Syst. Appl., 35, 1593, 10.1016/j.eswa.2007.08.072

Lei, 2008, Application of a novel hybrid intelligent method to compound fault diagnosis of locomotive roller bearings, J. Vib. Acoust., 130, 034501, 10.1115/1.2890396

Lei, 2007, Fault diagnosis of rotating machinery based on multiple ANFIS combination with Gas, Mech. Syst. Signal Process., 21, 2280, 10.1016/j.ymssp.2006.11.003

An, 2011, Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine, Energy, 36, 5508, 10.1016/j.energy.2011.07.025

S.F. Ai, H. Li, Y.P. Zhang, Condition monitoring for bearing using envelope spectrum of EEMD, International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China, April 11–12, 2009, 190–193.

Zvokelj, 2010, Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis, Mech. Syst. Signal Process., 24, 1049, 10.1016/j.ymssp.2009.09.002

Zvokelj, 2011, Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method, Mech. Syst. Signal Process., 25, 2631, 10.1016/j.ymssp.2011.03.002

Zhang, 2010, Performance enhancement of ensemble empirical mode decomposition, Mech. Syst. Signal Process., 24, 2104, 10.1016/j.ymssp.2010.03.003

X.M. Lu, J. Wang, Bearing fault diagnosis based on redundant second generation wavelet denoising and EEMD, International Conference on Consumer Electronics, Communications and Networks, XianNing, China, April 16–18, 2011, 1090–1093.

Lei, 2011, EEMD method and WNN for fault diagnosis of locomotive roller bearings, Expert Syst. Appl., 38, 7334, 10.1016/j.eswa.2010.12.095

W. Guo, P.W. Tse, Enhancing the ability of ensemble empirical mode decomposition in machine fault diagnosis, 2010 Progno sties & System Health Management Conference, Macao, January 12–14, 2010, 1–7.

Li, 2006, Wear detection in gear system using Hilbert–Huang Transform, J. Mech. Sci. Technol., 20, 1781, 10.1007/BF03027572

Cheng, 2008, Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis, Mech. Mach. Theory, 43, 712, 10.1016/j.mechmachtheory.2007.05.007

Loutridis, 2004, Damage detection in gear systems using empirical mode decomposition, Eng. Struct., 26, 1833, 10.1016/j.engstruct.2004.07.007

Loutridis, 2006, Instantaneous energy density as a feature for gear fault detection, Mech. Syst. Signal Process., 20, 1239, 10.1016/j.ymssp.2004.12.001

Pareya, 2006, Dynamic modeling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect, J. Sound Vib., 294, 547, 10.1016/j.jsv.2005.11.021

Parey, 2007, Impact velocity modeling and signal processing of spur gear vibration for the estimation of defect size, Mech. Syst. Signal Process., 21, 234, 10.1016/j.ymssp.2005.12.011

J.Z. Wang, G.H. Zhou, X.S. Zhao, et al., Gearbox fault diagnosis and prediction based on empirical mode decomposition scheme, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, August 19–22, 2007, 1072–1075.

H. Li, L.H. Fu, Z.T. Li, Gear fault detection using angle domain average and Hilbert–Huang transform phase map, The 2nd International Congress on Image and Signal Processing, Tianjin, China, October 17–19, 2009,1–5.

Ibrahim, 2011, Comparison between Wigner–Ville distribution and empirical mode decomposition vibration-based techniques for helical gearbox monitoring, J. Mech. Eng. Sci., 225, 1833, 10.1177/0954406211403571

Wang, 2011, An empirical re-sampling method on intrinsic mode function to deal with speed variation in machine fault diagnostics, Appl. Soft Comput., 11, 5015, 10.1016/j.asoc.2011.05.056

Shao, 2011, Gear damage detection and diagnosis system based on COM module, Procedia Eng., 15, 2301, 10.1016/j.proeng.2011.08.431

Yang, 2009, A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach, Measurement, 42, 542, 10.1016/j.measurement.2008.09.011

He, 2011, Machine fault signature analysis by midpoint-based empirical mode decomposition, Meas. Sci. Technol., 22, 015702, 10.1088/0957-0233/22/1/015702

Liu, 2006, Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum, Mech. Syst. Signal Process., 20, 718, 10.1016/j.ymssp.2005.02.003

Cheng, 2007, Application of support vector regression machines to the processing of end effects of Hilbert–Huang transform, Mech. Syst. Signal Process., 21, 1197, 10.1016/j.ymssp.2005.09.005

Qin, 2011, A new method for multicomponent signal decomposition based on self-adaptive filtering, Measurement, 44, 1312, 10.1016/j.measurement.2011.02.014

Cheng, 2006, Research on the intrinsic mode function (IMF) criterion in EMD method, Mech. Syst. Signal Process., 20, 817, 10.1016/j.ymssp.2005.09.011

Parey, 2012, Variable cosine windowing of intrinsic mode functions: application to gear fault diagnosis, Measurement, 45, 415, 10.1016/j.measurement.2011.11.001

Wang, 2010, Improvement of local mean approximation in empirical mode decomposition for gear fault detection, Eksploatacja I Niezawodnosc-Maint. Reliab., 46, 59

Ricci, 2011, Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions, Mech. Syst. Signal Process., 25, 821, 10.1016/j.ymssp.2010.10.002

Y.Y. Li, A discussion on using empirical mode decomposition for incipient fault detection and diagnosis of the wind turbine gearbox, World Non-Grid-Connected Wind Power and Energy Conference, Nanjing, China, November 5–7, 2010,1–5.

Li, 2010, Gear fault detection based on Teager–Huang transform, Int. J. Rotating Mach., 2010, 1, 10.1155/2010/502064

Zamanian, 2011, Gear fault diagnosis based on Gaussian correlation of vibrations signals and wavelet coefficients, Appl. Soft Comput., 11, 4807, 10.1016/j.asoc.2011.06.020

Li, 2012, Rotational machine health monitoring and fault detection using EMD-based acoustic emission feature quantification, IEEE Trans. Instrum. Meas., 61, 990, 10.1109/TIM.2011.2179819

Cheng, 2008, A fault diagnosis approach for gears based on IMF AR model and SVM, EURASIP J. Adv. Signal Process., 2008, 1, 10.1155/2008/647135

Lei, 2010, A multidimensional hybrid intelligent method for gear fault diagnosis, Expert Syst. Appl., 37, 1419, 10.1016/j.eswa.2009.06.060

Shen, 2012, A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM, Measurement, 45, 30, 10.1016/j.measurement.2011.10.008

S.F. Ai, H. Li, Gear Fault Detection Based on Ensemble Empirical Mode Decomposition and Hilbert–Huang Transform, Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Shandong, China, October 18–20, 2008, 173–177.

L.Y. Guan, Y.M. Shao, F.S. Gu, et al., Gearbox fault diagnosis under different operating conditions based on Time Synchronous Average and Ensemble Empirical Mode Decomposition, ICROS–SICE International Joint Conference 2009, Fukuoka, Japan, August 18–21, 2009, 383–388.

Y.G. Lei, M.J. Zuo, M. Hoseini, Rotating machinery fault detection using and bispectrum, Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2009, San Diego, California, USA, August 30–September 2, 2009, 1–6.

Lei, 2010, The use of ensemble empirical mode decomposition to improve bispectral analysis for fault detection in rotating machinery, Proce. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci., 224, 1759, 10.1243/09544062JMES1827

J.S. Lin, Q. Chen, Application of the EEMD method to multiple faults diagnosis of gearbox, Proceedings of the Second International Conference on Advanced Computer Control, Shenyang, China, March 27–29, 2010, 395–399.

Zhou, 2011, Feed–axis gearbox condition monitoring using built-in position sensors and EEMD method, Robotics and Comput.-Integrated Manuf., 27, 785, 10.1016/j.rcim.2010.12.001

Zhao, 2011, Fault diagnosis of a machine tool rotary axis based on a motor current test and the ensemble empirical mode decomposition method, J. Mech. Eng. Sci., 225, 1121, 10.1177/09544062JMES2368

Yang, 2009, Empirical mode decomposition, an adaptive approach for interpreting shaft vibratory signals of large rotating machinery, J. Sound Vib., 321, 1144, 10.1016/j.jsv.2008.10.012

Wang, 2010, A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis, J. Vib. Acoust., 132, 021010, 10.1115/1.4000770

Gai, 2006, The processing of rotor startup signals based on empirical mode decomposition, Mech. Syst. Signal Process., 20, 222, 10.1016/j.ymssp.2004.07.001

Chan, 2009, A novel fast reliable data transmission algorithm for wireless machine health monitoring, IEEE Trans. Reliab., 58, 295, 10.1109/TR.2009.2020479

Patel, 2009, Coupled bending–torsional vibration analysis of rotor with rub and crack, J. Sound Vib., 326, 740, 10.1016/j.jsv.2009.05.020

Wu, 2009, Diagnosis of subharmonic faults of large rotating machinery based on EMD, Mech. Syst. Signal Process., 23, 467, 10.1016/j.ymssp.2008.03.007

Lee, 2008, Fault diagnosis of partial rub and looseness in rotating machinery using Hilbert–Huang transform, J. Mech. Sci. Technol., 22, 2151, 10.1007/s12206-008-0714-0

Han, 2008, Periodic motions of a dual-disc rotor system with rub-impact at fixed limiter, Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci., 222, 1935, 10.1243/09544062JMES947

Lei, 2009, Application of an intelligent classification method to mechanical fault diagnosis, Expert Syst. Appl., 36, 9941, 10.1016/j.eswa.2009.01.065

Lin, 2011, Condition-based shaft fault diagnosis with the empirical mode decomposition method, J. Eng. Manuf., 225, 723, 10.1177/2041297510394062

Yang, 2005, Non-linear characteristics of a cracked rotor–journal bearing system, Proc. Inst. Mech. Eng., Part K: J. Multi-body Dyn.s, 219, 87

Lin, 2012, HHT-based AE characteristics of natural fatigue cracks in rotating shafts, Mech. Syst. Signal Process., 26, 181, 10.1016/j.ymssp.2011.07.017

Guo, 2007, Vibration analysis of a cracked rotor using Hilbert–Huang transform, Mech. Syst. Signal Process., 21, 3030, 10.1016/j.ymssp.2007.05.004

Wu, 2008, An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery, J. Sound and Vib., 314, 586, 10.1016/j.jsv.2008.01.020

Yang, 2011, Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring, J. Sound Vib., 330, 3766, 10.1016/j.jsv.2011.02.027

Qi, 2007, Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis, Mech. Syst. Signal Process., 21, 2750, 10.1016/j.ymssp.2007.04.007

Wu, 2010, Looseness diagnosis of rotating machinery via vibration analysis through Hilbert–Huang transform approach, J. Vib. Acoust., 132, 031005, 10.1115/1.4000782

Gao, 2008, Rotating machine fault diagnosis using empirical mode decomposition, Mech. Syst. Signal Process., 22, 1072, 10.1016/j.ymssp.2007.10.003

Peng, 2005, An improved Hilbert–Huang transform and its application in vibration signal analysis, J. Sound Vib., 286, 187, 10.1016/j.jsv.2004.10.005

Dong, 2009, Rotor crack detection based on high-precision modal parameter identification method and wavelet finite element model, Mech. Syst. Signal Process., 23, 869, 10.1016/j.ymssp.2008.08.003

Zhao, 2012, Multivariate EMD and full spectrum based condition monitoring for rotating machinery, Mech. Syst. Signal Process., 27, 712, 10.1016/j.ymssp.2011.08.001

Bin, 2012, Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network, Mech. Syst. Signal Process., 27, 696, 10.1016/j.ymssp.2011.08.002

Y.B. Li, F.L. Meng, Y.J. Lu, Research on Rub Impact Fault Diagnosis Method of Rotating Machinery Based on EMD and SVM, Proceedings of the 2009 IEEE International Conference on Mechatronics and Automation, Changchun, China, August 9–12, 2009, 4806–4810.

Wu, 2012, A looseness identification approach for rotating machinery based on post-processing of ensemble empirical mode decomposition and autoregressive modeling, J. Vib. Control., 18, 1, 10.1177/1077546311411755

Lei, 2009, Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs, Meas. Sci. Technol., 20, 2280, 10.1088/0957-0233/20/12/125701

Wu, 2009, Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach, Smart Mater. Struct., 18, 095004, 10.1088/0964-1726/18/9/095004

S.K. Yadav, P.K. Kalra, Fault diagnosis of internal combustion Engine using Empirical Mode Decomposition, Proceedings of the 6th International Symposium on Image and Signal Processing and Analysis, Salzburg, Austria, September 16–18, 2009, 40–46.

Chen, 2007, Vibration-based damage detection in composite wingbox structures by HHT, Mech. Syst. Signal Process., 21, 307, 10.1016/j.ymssp.2006.03.013

Chen, 2007, Early damage detection in composite wingbox structures using Hilbert–Huang transform and genetic algorithm, Struct. Health Monit., 6, 281, 10.1177/1475921707081970

J. Antonino–Daviu, S. Aviyente, E.G. Strangas, et al., An EMD-Based Invariant Feature Extraction Algorithm for Rotor Bar Condition Monitoring, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives, Bologna, Italy, September 5–8, 2011, 669–675.

Kalvoda, 2010, A cutter tool monitoring in machining process using Hilbert–Huang transform, Int. J. Mach. Tools Manuf., 50, 495, 10.1016/j.ijmachtools.2010.01.006

Rezaei, 2011, Damage identification in beams using empirical mode decomposition, Struct. Health Monit., 10, 261, 10.1177/1475921710373298

Braun, 2011, Decomposition of non–stationary signals into varying time scales: some aspects of the EMD and HVD methods, Mech. Syst.d Signal Process., 25, 2608, 10.1016/j.ymssp.2011.04.005

Lin, 2009, Application of empirical mode decomposition in the impact-echo test, NDT&E Int., 42, 589, 10.1016/j.ndteint.2009.03.003

Kalvoda, 2010, Analysis of signals for monitoring of nonlinear and non-stationary machining processes, Sensors and Actuators A, 161, 39, 10.1016/j.sna.2010.05.032

Bassiuny, 2007, Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator, Int. J. Mach. Tools Manuf., 47, 1011, 10.1016/j.ijmachtools.2006.06.016

Antonino-Daviu, 2009, Transient detection of eccentricity–related components in induction motors through the Hilbert–Huang Transform, E. Convers. Manag., 50, 1810, 10.1016/j.enconman.2009.03.008

Zhang, 2005, Dynamic response of the Trinity River Relief Bridge to controlled pile damage: modeling and experimental data analysis comparing Fourier and Hilbert–Huang techniques, J. Sound Vib., 285, 1049, 10.1016/j.jsv.2004.09.032

Li, 2010, EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine, Mech. Syst. Signal Process., 24, 193, 10.1016/j.ymssp.2009.06.012

Bao, 2010, EMD-based extraction of modulated cavitation noise, Mech. Syst. Signal Process., 24, 2124, 10.1016/j.ymssp.2010.03.013

J.S. Lin, Fault Diagnosis of Natural Gas Compressor Based on EEMD and Hilbert Marginal Spectrum, 2010, Proceedings of the Second International Conference on Information Science and Engineering, Hangzhou, China, December 4–6, 2010, 3701–3704.

Li, 2007, Structural damage detection using the combination method of EMD and wavelet analysis, Mech. Syst. Signal Process., 21, 298, 10.1016/j.ymssp.2006.05.001

Yang, 2008, Interpretation of mechanical signals using an improved Hilbert–Huang transform, Mech. Syst. Signal Process., 22, 1061, 10.1016/j.ymssp.2007.11.024

Guo, 2008, Application of EMD method to friction signal processing, Mech. Syst. Signal Process., 22, 248, 10.1016/j.ymssp.2007.07.002

Bassiuny, 2007, Fault diagnosis of stamping process based on empirical mode decomposition and learning vector quantization, Int. J. Mach. Tools Manuf., 47, 2298, 10.1016/j.ijmachtools.2007.06.006

Y.G. Chen, H.X. Zhang, Y.H. Shen, Method of EMD and ZOOM-FFT to Detect the Broken Bars Fault In Induction Motor, International Conference on Electrical Machines and Systems, Incheon, Korea, October 10–13, 2010, 1387–1391.

Wang, 2011, Application of computed order tracking, Vold–Kalman filtering and EMD in rotating machine vibration, Mech. Syst. Signal Process., 25, 416, 10.1016/j.ymssp.2010.09.003

Z.X. Shen, X.Y. Huang, X.X. Ma, An Intelligent Fault Diagnosis Method Based on Empirical Mode Decomposition and Support Vector Machine, Proceedings of the Third International Conference on Convergence and Hybrid Information Technology, Busan, Korea, November 11–13, 2008, 865–869.

Li, 2011, Extraction of oil debris signature using integral enhanced empirical mode decomposition and correlated reconstruction, Meas. Sci. Technol., 22, 085701, 10.1088/0957-0233/22/8/085701

Wu, 2011, A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower, Expert Syst. Appl., 38, 545, 10.1016/j.eswa.2010.06.100

Z.W. Sun, G. Shi, X.P. Liu, Structural Damage Identification Method Based on IMF Model Energy Feature and BP Neural Network, International Conference on Intelligence Science and Information Engineering, Wuhan, China, August 20–21, 2011, 206–209.

Zhou, 2012, Vibration fault diagnosis method of centrifugal pump based on EMD complexity feature and least square support vector machine, Energy Procedia, 17, 939, 10.1016/j.egypro.2012.02.191