Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples

Mechanical Systems and Signal Processing

Volumn 38, Issue 1, 2013, Pages 165-205

  Feng, Zhipeng ^a   Liang, Ming ^b   Chu, Fulei ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^b UNIVERSITY OF OTTAWA   (Canada)

^c TSINGHUA UNIVERSITY   (China)

Author keywords

Fault diagnosis; Nonstationary signal; Time frequency analysis

Indexed keywords

AUTO-REGRESSIVE MOVING AVERAGE MODEL; LOCAL MEAN DECOMPOSITIONS; MACHINERY FAULT DIAGNOSIS; NON-STATIONARY SIGNAL ANALYSIS; NONSTATIONARY SIGNALS; TIME FREQUENCY ANALYSIS; TIME-FREQUENCY ANALYSIS METHODS; TIME-FREQUENCY REPRESENTATIONS;

FAILURE ANALYSIS; SIGNAL PROCESSING;

MACHINERY;

EID: 84876940227     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2013.01.017     Document Type: Article

References (107)

1. F. Chu, Z. Peng, Z. Feng, and Z. Li Modern Signal Processing Methods in Machinery Fault Diagnosis 2009 Science Press Beijing in Chinese
2. J. Hammond, and P. White The analysis of non-stationary signals using time-frequency methods J. Sound Vib. 190 1996 419 447
3. L. Atlas, G. Bernard, S. Narayanan, Applications of time-frequency analysis to signals from manufacturing and machine monitoring sensors, Proc. IEEE, 1996, 84: 1319-1329
4. L. Cohen Time-Frequency Analysis 1995 Prentice Hall Englewood Cliffs, NJ
5. S. Qian, and D. Chen Joint Time-Frequency Analysis - Methods and Applications 1996 Prentice Hall Englewood Cliffs, NJ
6. P. Flandrin Time-Frequency/Time-Scale Analysis 1999 Academic Press San Diego, CA
7. K. Grochenig Foundations of Time-Frequency Analysis 2000 Birkhauser Basel
8. B. Boashash Time-Frequency Signal Analysis and Processing 2002 Prentice Hall New York
9. A. Papandreou-Suppappola Applications in Time-Frequency Signal Processing 2003 CRC Press Boca Raton, FL
10. F. Hlawatsch, and G.F. Boudreaux-Bartels Linear and quadratic time-frequency signal representations IEEE Signal Process. Mag. 9 2 1992 21 67
11. N. Hess-Nielsen, and M.V. Wickerhauser Wavelets and time-frequency analysis Proc. IEEE 84 4 1996 523 540
12. I. Daubechies Ten Lectures on Wavelets 1992 SIAM Philadelphia, PA
13. S.G. Mallat A Wavelet Tour of Signal Processing: the sparse way 2009 Academic Press San Diego, CA
14. L. Cohen Time-frequency distributions: a review Proc. IEEE 77 1989 941 981
15. P.Flandrin Rioul Time-scale energy distributions: a general class extending wavelet transforms IEEE Trans. Signal Process. 40 7 1992 1746 1757
16. P. Flandrin, and P. Goncalves Geometry of affine time-frequency distributions Appl. Comput. Harmonic Anal. 3 1996 10 39
17. R.G. Baraniuk, and D.L. Jones Signal-dependent time frequency representation: optimal kernel design IEEE Trans. Signal Process. 41 1993 1589 1602
18. D.L. Jones, and R.G. Baraniuk An adaptive optimal kernel time-frequency representation IEEE Trans. Signal Process. 43 1995 2361 2371
19. F. Auger, and P. Flandrin Improving the readability of time-frequency and time-scale representations by the reassignment method IEEE Trans. Signal Process. 43 5 1995 1068 1089
20. R. Fonollosa, and C.L. Nikias Wigner higher order moment spectra: definition, properties, computation and application to transient signal analysis IEEE Trans. Signal Process. 41 1993 245 266
21. R. Fonollosa, and C.L. Nikias Analysis of finite-energy signals using higher-order moments- and spectra-based time-frequency distributions Signal Process. 36 1994 315 328
22. L.J. Stankovic L-class of time-frequency distributions IEEE Signal Process. Lett. 3 1 1996 22 25
23. L.J. Stankovic, S-class of distributions, in: IEE Proceedings - Vision, Image and Signal Processing, vol. 144(2), 1997. pp.57-64
24. I. Daubechies The wavelet transform, time-frequency localization and signal analysis IEEE Trans. Inf. Theory 36 5 1990 961 1005
25. R.R. Coifman, and M.V. Wickerhauser Entropy-based algorithms for best basis selection IEEE Trans. Inf. Theory 38 1992 713 718
26. S.G. Mallat, and Z. Zhang Matching pursuit with time-frequency dictionaries IEEE Trans. Signal Process. 41 1993 3397 3415
27. S. Qian, and D. Chen Signal representation using adaptive normalized Gaussian functions Signal Process. 36 1 1994 1 11
28. G. Davis, S. Mallat, and M. Avellaneda Adaptive greedy approximations Contstructive Approx. 13 1997 57 98
29. S. Jaggi, W.C. Karl, S. Mallat, and A.S. Willsky High resolution pursuit for feature extraction Appl. Comput. Harmonic Anal. 5 1998 428 449
30. S.S. Chen, D.L. Donoho, and M.A. Saunders Atomic decomposition by basis pursuit SIAM J. Sci. Comput. 20 1 1998 33 61
31. A. Bultan A four-parameter atomic decomposition of chirplets IEEE Trans. Signal Process. 47 1999 731 744
32. Q. Yin, S. Qian, and A. Feng A fast refinement for adaptive Gaussian chirplet decomposition IEEE Trans. Signal Process. 50 2002 1298 1306
33. H. Zou, Q. Dai, R. Wang, and Y. Li Parametric TFR via windowed exponential frequency modulated atoms IEEE Signal Process. Lett. 8 2001 140 142
34. H. Zou, Y. Chen, J. Zhu, Q. Dai, G. Wu, and Y. Li Steady-motion-based Dopplerlet transform Application to the estimation of range and speed of a moving sound source IEEE J. Ocean. Eng. 29 3 2004 887 905
35. M. Jachan, G. Matz, and F. Hlawatsch Time-frequency ARMA models and parameter estimators for underspread nonstationary random processes IEEE Trans. Signal Process. 55 9 2007 4366 4381
36. N.E. Huang, Z. Shen, S.R. Long, et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Proc. R. Soc. London Ser. A 1998, 454: 903-995
37. N.E. Huang, Z. Shen, and S.R. Long A new view of nonlinear water waves: the Hilbert spectrum Annu. Rev. Fluid Mech. 31 1999 417 457
38. N.E. Huang, M.C. Wu, S.R. Long, et al., A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proc. R. Soc. London Ser. A, 2003, 459: 2317-2345
39. N.E. Huang, and S.P. Shen 2005 World Scientific Singapore
40. N.E. Huang, and Z. Wu A review on Hilbert-Huang transform: method and its applications to geophysical studies Rev. Geophys. 46 2008 1 23 RG2006
41. Z. Wu, and N.E. Huang Ensemble empirical mode decomposition: a noise-assisted data analysis method Adv. Adaptive Data Anal. 1 1 2009 1 41
42. S. Smith The local mean decomposition and its application to EEG perception data J. R. Soc. Interface 2 2005 443 454
43. P. Maragos, J.F. Kaiser, and T.F. Quatieri On amplitude and frequency demodulation using energy operators IEEE Trans. Signal Process. 41 4 1993 1532 1550
44. P. Maragos, J.F. Kaiser, and T.F. Quatieri Energy separation in signal modulations with application to speech analysis IEEE Trans. Signal Process. 41 10 1993 3024 3051
45. C. Bovik, P. Maragos, and T.F. Quatieri AM-FM energy detection and separation in noise using multiband energy operators IEEE Trans. Signal Process. 41 12 1993 3245 3265
46. A. Potamianos, and P. Maragos A comparison of the energy operator and Hilbert transform approaches for signal and speech demodulation Signal Process. 37 No. 1 1994 95 120
47. Z. He, and Y. Zi Fault Diagnosis Principles of Nonstationary Signal and Applications to Mechanical Equipment 2001 Higher Education Press Beijing in Chinese
48. Z. Peng, and F. Chu Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography Mech. Syst. Signal Process. 18 2004 199 221
49. W.J. Wang, and P.D. McFadden Early detection of gear failure by vibration analysis - I: calculation of the time-frequency distribution Mech. Syst. Signal Process. 7 1993 193 203
50. W.J. Wang, and P.D. McFadden Early detection of gear failure by vibration analysis - II: interpretation of the time-frequency distribution using image processing techniques Mech. Syst. Signal Process. 7 1993 205 215
51. P.J. Loughlin Cohen-Posch (positive) time-frequency distributions and their application to machine vibration analysis Mech. Syst. Signal Process. 11 4 1997 561 576
52. H. Oehlmann, D. Brie, M. Tomczak, and A. Richard A method for analysing gearbox faults using time-frequency representations Mech. Syst. Signal Process. 11 4 1997 529 545
53. W.J. Staszewski, K. Worden, and G.R. Tomlinson Time-frequency analysis in gearbox fault detections using the Wigner-Ville distribution and pattern recognition Mech. Syst. Signal Process. 11 1997 673 692
54. W.J. Williams, and E.J. Zalubas Helicopter transmission fault detection via time-frequency, scale and spectral methods Mech. Syst. Signal Process. 14 2000 545 559
55. J. Zou, and J. Chen A comparative study on time-frequency feature of cracked rotor by Wigner-Ville distribution and wavelet transform J. Sound Vib. 276 1-2 2004 1 11
56. N. Le Time-frequency distributions for crack detection in rotors - A fundamental note J. Sound Vib. 294 1-2 2006 397 409
57. A. Adewusi, and B.O. Al-Bedoor Wavelet analysis of vibration signals of an overhang rotor with a propagating transverse crack J. Sound Vib. 246 2001 777 793
58. H. Zheng, Z. Li, and X. Chen Gear fault diagnosis based on continuous wavelet transform Mech. Syst. Signal Process. 16 2-3 2002 447 457
59. I. Yesilyurt The application of the conditional moments analysis to gearbox fault detection - a comparative study using the spectrogram and scalogram NDT&E Int. 37 2004 309 320
60. B. Sun, E. Wang, Y. Ding, H. Bai, and Y. Huang Time-frequency signal processing for gas-liquid two phase flow through a horizontal venturi based on adaptive optimal-kernel theory Chin. J. Chem. Eng. 19 2 2011 243 252
61. Z. Peng, F. Chu, and Y. He Vibration signal analysis and feature extraction based on reassigned wavelet scalogram J. Sound Vib. 253 5 2002 1087 1100
62. Z. Peng, F. Chu, and P.W. Tse Detection of the rubbing-caused impacts for rotor-stator fault diagnosis using reassigned scalogram Mech. Syst. Signal Process. 19 2 2005 391 409
63. H. Ma, T. Yu, Q. Han, Y. Zhang, B. Wen, and X. Chen Time-frequency features of two types of coupled rub-impact faults in rotor systems J. Sound Vib. 321 2009 1109 1128
64. S. Lee, and P. White Higher-order time-frequency analysis and its application to fault detection in rotating machinery Mech. Syst. Signal Process. 11 4 1997 637 650
65. S. Lee Fault diagnosis of a gearbox using the sliced Wigner fourth order time frequency method smoothed by a new kernel function KSME Int. J. 13 12 1999 940 947
66. B. Liu, S. Ling, and R. Gribonval Bearing failure detection using matching pursuit NDT&E Int. 35 2002 255 262
67. D. Shi, F. Tsung, and P.J. Unsworth Adaptive time-frequency decomposition for transient vibration monitoring of rotating machinery Mech. Syst. Signal Process. 18 2004 127 141
68. L. Cui, C. Kang, H. Wang, and P. Chen Application of composite dictionary multi-atom matching in gear fault diagnosis Sensors 11 2011 5981 6002
69. H. Yang, J. Mathew, and L. Ma Fault diagnosis of rolling element bearings using basis pursuit Mech. Syst. Signal Process. 19 2005 341 356
70. L.R. Padovese Hybrid time-frequency methods for non-stationary mechanical signal analysis Mech. Syst. Signal Process. 18 2004 1047 1064
71. G. Poulimenos, and S.D. Fassois Parametric time-domain methods for non-stationary random vibration modelling and analysis - A critical survey and comparison Mech. Syst. Signal Process. 20 4 2006 763 816
72. D. Yu, J. Cheng, and Y. Yang Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings Mech. Syst. Signal Process. 19 2 2005 259 270
73. J. Cheng, D. Yu, J.S. Tang, and Y. Yang Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis Mech. Mach. Theory 43 6 2008 712 723
74. J. Cheng, D. Yu, J. Tang, and Y. Yang Local rub-impact fault diagnosis of the rotor systems based on EMD Mech. Mach. Theory 44 4 2009 784 791
75. Z. Peng, P.W. Tse, and F. Chu A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing Mech. Syst. Signal Process. 19 2005 974 988
76. Z. Peng, P.W. Tse, and F. Chu An improved Hilbert-Huang transform and its application in vibration signal analysis J. Sound Vib. 286 1-2 2005 187 205
77. D. Guo, and Z. Peng Vibration analysis of a cracked rotor using Hilbert-Huang transform Mech. Syst. Signal Process. 21 8 2007 3030 3041
78. W. Yang Interpretation of mechanical signals using an improved Hilbert-Huang transform Mech. Syst. Signal Process. 22 5 2008 1061 1071
79. W. Yang, and P.J. Tavner Empirical mode decomposition, an adaptive approach for interpreting shaft vibratory signals of large rotating machinery J. Sound Vib. 321 3-5 2009 1144 1170
80. F. Wu, and L. Qu 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 Vib. 14 3-5 2008 586 602
81. F. Wu, and L. Qu Diagnosis of subharmonic faults of large rotating machinery based on EMD Mech. Syst. Signal Process. 23 2 2009 467 475
82. Q. Gao, C. Duan, H. Fan, and Q. Meng Rotating machine fault diagnosis using empirical mode decomposition Mech. Syst. Signal Process. 22 5 2008 1072 1081
83. G. Gai The processing of rotor startup signals based on empirical mode decomposition Mech. Syst. Signal Process. 20 1 2006 222 235
84. T. Ramesh Babu, S. Srikanth, and A.S. Sekhar Hilbert-Huang transform for detection and monitoring of crack in a transient rotor Mech. Syst. Signal Process. 22 4 2008 905 914
85. S.J. Loutridis Damage detection in gear systems using empirical mode decomposition Eng. Struct. 26 2004 1833 1841
86. B. Liu, S. Riemenschneider, and Y. Xu Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum Mech. Syst. Signal Process. 20 3 2006 718 734
87. R. Ricci, and P. Pennacchi Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions Mech. Syst. Signal Process. 25 3 2011 821 838
88. Y. Li, P.W. Tse, X. Yang, and J. Yang EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine Mech. Syst. Signal Process. 24 1 2010 193 210
89. Y. Lei, Z. He, and Y. Zi Application of the EEMD method to rotor fault diagnosis of rotating machinery Mech. Syst. Signal Process. 23 4 2009 1327 1338
90. Y. Lei, and M.J. Zuo Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs Meas. Sci. Technol. 20 12 2009 1 12
91. J. Zhang, R. Yan, R.X. Gao, and Z. Feng Performance enhancement of ensemble empirical mode decomposition Mech. Syst. Signal Process. 24 7 2010 2104 2123
92. M. Zvokelj, S. Zupan, and I. Prebil 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 4 2010 1049 1067
93. X. Zhao, T.H. Patel, and M.J. Zuo Multivariate EMD and full spectrum based condition monitoring for rotating machinery Mech. Syst. Signal Process. 27 2012 712 728
94. Y. Wang, Z. He, and Y. Zi A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis Meas. Sci. Technol. 20 2009 025704
95. Y. Wang, Z. He, J. Xiang, and Y. Zi Application of local mean decomposition to the surveillance and diagnostics of low-speed helical gearbox Mech. Mach. Theory 47 2012 62 73
96. J. Cheng, and Y. Yang A rotating machinery fault diagnosis method based on local mean decomposition Digital Signal Process. 22 2012 356 366
97. Y. Yang, J. Cheng, and K. Zhang An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems Measurement 45 3 2012 561 570
98. J. Cheng, D. Yu, and Y. Yang The application of energy operator demodulation approach based on EMD in machinery fault diagnosis Mech. Syst. Signal Process. 21 2 2007 668 677
99. M. Bassiuny, and X. Li Flute breakage detection during end milling using Hilbert-Huang transform and smoothed nonlinear energy operator Int. J. Mach. Tools Manuf. 47 6 2007 1011 1020
100. H. Li, H. Zheng, and L. Tang Gear fault detection based on Teager-Huang transform Int. J. Rotating Mach. 2010 2010 502064
101. M. Liang, and I. Soltani Bozchalooi An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection Mech. Syst. Signal Process. 24 5 2010 1473 1494
102. I. Soltani Bozchalooi, and M. Liang Teager energy operator for multi-modulation extraction and its application for gearbox fault detection Smart Mater. Struct. 19 2010 075008
103. Z. Feng, and F. Chu Nonstationary vibration signal analysis of hydroturbine based on adaptive chirplet decomposition Struct. Health Monit. 6 4 2007 265 279
104. Z. Feng, and F. Chu Application of atomic decomposition to gear damage detection J. Sound Vib. 302 2007 138 151
105. Z. Feng, and F. Chu Application of adaptive time-frequency analysis to fault diagnosis of machinery Chin. J. Sci. Instrum. 29 4 2008 604 608
106. Z. Feng, F. Chu, Analysis of transient pressure fluctuation in hydroturbine draft tube based on Hilbert-Huang transform, in: Proceedings of the 3rd World Congress on Engineering Asset Management and Intelligent Maintenance Systems (WCEAM-IMS 2008), Beijing: 27-30 October, 2008, pp. 463-468
107. Z. Feng, F. Chu, and M.J. Zuo Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation J. Sound Vib. 330 2011 1225 1243