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Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

Chen, Jinglong, Li, Zipeng, Pan, Jun, Chen, Gaige, Zi, Yanyang, Yuan, Jing, Chen, Binqiang, He, Zhengjia·2016·Mechanical Systems and Signal Processing
DOI 10.1016/j.ymssp.2015.08.023
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Research works printed but not linked: 96

  1. Ding, 2010, Parameter estimation accuracy of FFT and FT discrete spectrum correction, J. Mech. Eng., 46, 68, 10.3901/JME.2010.07.068 · doi:10.3901/jme.2010.07.068
  2. Narasimhan, 2004, Estimation of evolutionary spectrum based on short time Fourier transform and modified group delay, Signal Process., 84, 2139, 10.1016/j.sigpro.2004.07.004 · doi:10.1016/j.sigpro.2004.07.004
  3. Giv, 2013, Directional short-time Fourier transform, J. Math. Anal. Appl., 399, 100, 10.1016/j.jmaa.2012.09.053 · doi:10.1016/j.jmaa.2012.09.053
  4. Li, 2014, Wavelet-based numerical analysis: a review and classification, Finite Elem. Anal. Des., 81, 14, 10.1016/j.finel.2013.11.001 · doi:10.1016/j.finel.2013.11.001
  5. Li, 2008, Rotating machinery fault diagnosis using signal-adapted lifting scheme, Mech. Syst. Signal Process., 22, 542, 10.1016/j.ymssp.2007.09.008 · doi:10.1016/j.ymssp.2007.09.008
  6. Xiao, 2014, A novel approach to machining condition monitoring of deep hole boring, Int. J. Mach. Tools Manuf., 77, 27, 10.1016/j.ijmachtools.2013.10.009 · doi:10.1016/j.ijmachtools.2013.10.009
  7. Wang, 2014, Feature extraction and classification of load dynamic characteristics based on lifting wavelet packet transform in power system load modeling, Int. J. Electr. Power Energy Syst., 62, 353, 10.1016/j.ijepes.2014.04.051 · doi:10.1016/j.ijepes.2014.04.051
  8. Donovan, 1996, Construction of orthogonal wavelets using fractal interpolation functions, SIAM J. Math. Anal., 27, 1158, 10.1137/S0036141093256526 · doi:10.1137/s0036141093256526
  9. Cao, 2013, Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform, Int. J. Mach. Tools Manuf., 69, 11, 10.1016/j.ijmachtools.2013.02.007 · doi:10.1016/j.ijmachtools.2013.02.007
  10. He, 2007, Transform principle of inner product for fault diagnosis, J. Vib. Eng., 20, 528
  11. He, 2012, Inner product principle of mechanical fault diagnosis and validation, J. Vib. Meas. Diagn., 32, 175
  12. Zhang, 2011, Frames, Riesz bases, and sampling expansions in Banach spaces via semi-inner products, Appl. Comput. Harmonic Anal., 31, 1, 10.1016/j.acha.2010.09.007 · doi:10.1016/j.acha.2010.09.007
  13. Li, 2009, Frame fundamental sensor modeling and stability of one-sided frame perturbation, Acta. Appl. Math., 107, 91, 10.1007/s10440-008-9419-8 · doi:10.1007/s10440-008-9419-8
  14. Lin, 2003, Gearbox fault diagnosis using adaptive wavelet filter, Mech. Syst. Signal Process., 17, 1259, 10.1006/mssp.2002.1507 · doi:10.1006/mssp.2002.1507
  15. Meyer, 1986, Principe d'incertitude, bases hilbertiennes et algebres d'operateurs, Séminaire Bourbaki, 662, 209
  16. Mallat, 1989, Multiresolution approximations and wavelet orthonormal bases of L2(R), Trans. Am. Mathe. Soc., 315, 69
  17. Wu, 1996, Feature extraction and assessment using wavelet packets for monitoring of machining process, Mech. Syst. Signal Process., 10, 29, 10.1006/mssp.1996.0003 · doi:10.1006/mssp.1996.0003
  18. J.E. Lopez, R.R. Tenney, J.C. Deckert, Fault detection and identification using real-time wavelet feature extraction, in: Proceedings of IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, Philadelphia, PA, USA, 1994. · doi:10.1109/tfsa.1994.467254
  19. Zhu, 2009, Synchronous enhancement of periodic transients on polar diagram for machine fault diagnosis, Int. J. Wavel. Multiresolut. Inf. Process., 7, 427, 10.1142/S0219691309003008 · doi:10.1142/s0219691309003008
  20. D.M. Yang, Induction motor bearing fault detection with non-stationary signal analysis, in: Proceedings of IEEE International Conference on Mechatronics, Kumamoto, Japan, 2007. · doi:10.1109/icmech.2007.4279981
  21. Chen, 2009, New approach of features extraction for rotor faults from continuous wavelet transform scalogram, J. Aerosp. Power, 24, 793
  22. Xu, 2012, Fault diagnosis for gearbox based on wavelet transform, Modul. Mach. Tool Autom. Manuf. Tech., 2, 66
  23. Rajeeva, 2014, Energy-based damage index for concentrically braced steel structure using continuous wavelet transform, J. Constr. Steel Res., 103, 241, 10.1016/j.jcsr.2014.09.011 · doi:10.1016/j.jcsr.2014.09.011
  24. R.J. Hao, Z.P. Feng, F.L. Chu, Defects diagnosis of bearing by means of acoustic emission and continuous wavelet transform, in: Proceedings of the 8th International Conference on Damage Assessment of Structures, Beijing, China, 2009. · doi:10.4028/www.scientific.net/kem.413-414.651
  25. Q. Tian, Z.M. Mao, N. Yamauchi, Bearing condition monitoring using wavelets analysis for fault diagnosis, in: Proceedings of the 3rd International Conference on Wavelet Analysis and Its Applications, Chongqing, China, 2004. · doi:10.1142/9789812701695_0030
  26. Postalcioglu, 2006, Discrete wavelet analysis based fault detection, WSEAS Trans. Syst., 5, 2391
  27. Y.P. Guo, W.J. Yan, Z.J. Bao, Gear fault diagnosis of wind turbine based on discrete wavelet Transform, in: Proceedings of the 8th World Congress on Intelligent Control and Automation, Jinan, China, 2010.
  28. Ma, 2010, Investigation of aeroengine fault diagnosis based on discrete wavelet transform, Comput. Meas. Control, 18, 272
  29. Zhao, 2011, Fault diagnosis of roller bearing based on relative wavelet energy, J. Electron. Meas. Instr., 25, 44, 10.3724/SP.J.1187.2011.00044 · doi:10.3724/sp.j.1187.2011.00044
  30. Khajavi, 2014, Intelligent fault classification of rolling bearings using neural network and discrete wavelet transform, J. Vibroeng., 16, 761
  31. Geng, 1994, Vibrational diagnosis of machine parts using the wavelet packet technique, Br. J. Non-Destr. Test., 36, 11
  32. J.Y. Luan, H.Y. Kang, H.Q. Zhang, The application of order tracking analysis and BP neural networks in fault diagnosis of gearbox, in: Proceedings of the 6th International Symposium on Test and Measurement, Dalian, China, 2005.
  33. Liu, 2005, Selection of wavelet packet basis for rotating machinery fault diagnosis, J. Sound Vib., 284, 567
  34. G.H. Gao, Y. Zhu, et al., Intelligent fault identification based on wavelet packet energy analysis and SVM, in: Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision, Singapore, 2006.
  35. Chen, 2008, A new fault diagnosis method of rotating machinery, Shock Vib., 15, 585, 10.1155/2008/203621 · doi:10.1155/2008/203621
  36. H. Abu-Rub, A. Iqbal, et al., Rotor broken bar diagnostics in induction motor drive using wavelet packet transform and ANFIS classification, in: Proceedings of IEEE International Electric Machines & Drives Conference: Niagara Falls, ON, Canada, 2011. · doi:10.1109/iemdc.2011.5994622
  37. Li, 2012, Fault diagnosis of railway axlebox bearing based on wavelet packet and neural network, Appl. Mech. Mater., 226–228, 749, 10.4028/www.scientific.net/AMM.226-228.749 · doi:10.4028/www.scientific.net/amm.226-228.749
  38. Lu, 2015, Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery, Mech. Syst. Signal Process., 52–53, 393, 10.1016/j.ymssp.2014.07.024 · doi:10.1016/j.ymssp.2014.07.024
  39. Niu, 2015, Fault diagnosis of locomotive electro-pneumatic brake through uncertain bond graph modeling and robust on line monitoring, Mech. Syst. Signal Process., 50–51, 676, 10.1016/j.ymssp.2014.05.020 · doi:10.1016/j.ymssp.2014.05.020
  40. Feng, 2014, Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time-frequency analysis, Renew. Energy, 66, 468, 10.1016/j.renene.2013.12.047 · doi:10.1016/j.renene.2013.12.047
  41. F. Wang, D. Mi, Z.S. Xu, Engine weak signal correlation detection based on lifting wavelet, in: Proceedings of the 7th International Conference on Electronic Measurement and Instruments, Beijing, China, 2005.
  42. Hu, 2006, Intelligent diagnosis for incipient fault based on lifting wavelet package transform and support vector machines ensemble, Chin. J. Mech. Eng., 42, 16, 10.3901/JME.2006.08.016 · doi:10.3901/jme.2006.08.016
  43. Duan, 2006, Undecimated wavelet transform based on lifting scheme and its application in fault diagnosis, J. Mech. Strength, 28, 796
  44. He, 2009, The principle of second generation wavelet for milling cutter breakage detection, Sci. China Ser. E Technol. Sci., 52, 1312, 10.1007/s11431-008-0298-z · doi:10.1007/s11431-008-0298-z
  45. Gao, 2011, Roller bearing fault diagnosis based on nonlinear redundant lifting wavelet packet analysis, Sensors, 11, 260, 10.3390/s110100260 · doi:10.3390/s110100260
  46. Li, 2011, Mechanical faulty signal denoising using a redundant non-linear second-generation wavelet transform, Proc. Inst. Mech. Eng. C – J. Mech. Eng. Sci., 225, 799, 10.1243/09544062JMES2410 · doi:10.1243/09544062jmes2410
  47. K. Li, J.B. Zhou, et al., Research on fault diagnosis method of bearing based on second generation wavelet packets, in: Proceedings of International Conference on Computer Science and Information Processing (CSIP), Xi'an, China, 2012. · doi:10.1109/csip.2012.6308993
  48. B.Q.Chen, Z.S.Zhang, et al., Pseudo non-dyadic second generation wavelet tight frame for machine fault diagnosis, in: Proceedings of IEEE International Instrumentation and Measurement Technology Conference, Minneapolis, MN, 2013. · doi:10.1109/i2mtc.2013.6555655
  49. Z.J. Shen, X.F. Chen, Z.J. He, Hybrid intelligent fault diagnosis based on adaptive lifting wavelet and multi-class support vector machine, in: International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, China, 2010. · doi:10.1109/icwapr.2010.5576405
  50. P. Arpaia, E.De Matteis, et al., Evolutionary design of lifting scheme wavelet-packet adaptive filters for elevator fault detection, in: IEEE International Instrumentation & Measurement Technology Conference: Austin, TX, 2010. · doi:10.1109/imtc.2010.5488007
  51. J.X. Qu, Z.S. Zhang, J.P. Wen, et al., State recognition of the viscoelastic sandwich structure based on the adaptive redundant second generation wavelet packet transform, permutation entropy and the wavelet support vector machine, 23, 2014, p. 085004. · doi:10.1088/0964-1726/23/8/085004
  52. Dumitrescu, 2008, Optimization of symmetric self-hilbertian filters for the dual-tree complex wavelet transform, IEEE Signal Process. Lett., 15, 146, 10.1109/LSP.2007.913609 · doi:10.1109/lsp.2007.913609
  53. Zhang, 2011, A new phase-factor design method for Hilbert-Pairs of orthonormal wavelets, IEEE Signal Process. Lett., 18, 529, 10.1109/LSP.2011.2162235 · doi:10.1109/lsp.2011.2162235
  54. Tay, 2012, Symmetric self-Hilbertian filters via extended zero-pinning, Signal Process., 92, 392, 10.1016/j.sigpro.2011.08.004 · doi:10.1016/j.sigpro.2011.08.004
  55. A.F. Abdelnlnour, Hilbert-like tight frame wavelets with symmetric envelope, in: Proceedings of the Conference on Wavelets XII, San Diego, CA, 2007. · doi:10.1117/12.733192
  56. P. Steffen, W. Brandhuber, Generation of dual N-channel filterbanks with Hilbert-transformed motherwavelets, in: Proceedings of the 16th European Signal Processing Conference, Switzerland, 2008.
  57. Dumitrescu, 2010, Optimization of the higher density discrete wavelet transform and of its dual tree, IEEE Trans. Signal Process., 58, 583, 10.1109/TSP.2009.2032449 · doi:10.1109/tsp.2009.2032449
  58. Qin, 2010, Higher density wavelet frames with symmetric low-pass and band-pass filters, Signal Process., 90, 3219, 10.1016/j.sigpro.2010.05.027 · doi:10.1016/j.sigpro.2010.05.027
  59. X.G. Jia, H.Z. Huang, W.B. Wang, KPCA-based multisensor data fusion for machine fault diagnosis, in: Proceedings of the 1st International Conference on Maintenance Engineering, Chengdu, China, 2006.
  60. X.G. Liu, Z.X. Chen, Signal-denoising based on DT-CWT and its extraction to gear weak fault information, in: Proceedings of the Sixth World Congress on Intelligent Control and Automation, Dalian, China, 2006.
  61. Chen, 2007, Denoising method of block thresholding based on DT-CWT and its application in mechanical fault diagnosis, Chin. J. Mech. Eng., 43, 200, 10.3901/JME.2007.06.200 · doi:10.3901/jme.2007.06.200
  62. Chen, 2012, The application of dual tree complex wavelet transform in mechanical multi-component signal decomposition and feature extraction, Int. J. Compr. Eng., 1, 25
  63. Chen, 2013, Time-frequency domain construction of dual tree complex wavelets for assembly clearance detection of gear chains, J. Xi'an Jiaotong Univ., 47, 7
  64. Ai, 2011, Research on bearing fault diagnosis based on dual-tree complex wavelet transform, China Mech. Eng., 22, 2446
  65. Chen, 2012, Enhancement of weak feature extraction in mechinery fault diagnosis by using double density dual tree complex wavelet transform, Chin. J. Mech. Eng., 48, 56, 10.3901/JME.2012.09.056 · doi:10.3901/jme.2012.09.056
  66. Meng, 2013, Fault diagnosis of rolling bearing based on dual-tree complex wavelet transform and AR power spectrum, Adv. Mater. Res., 819, 271, 10.4028/www.scientific.net/AMR.819.271 · doi:10.4028/www.scientific.net/amr.819.271
  67. Chen, 2013, Detecting of transient vibration signatures using an improved fast spatial-spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery, Mech. Syst. Signal Process., 40, 1, 10.1016/j.ymssp.2013.03.021 · DOI
  68. Zhang, 2014, Periodic impulsive fault feature extraction of rotating machinery using dual-tree rational dilation complex wavelet transform, J. Manuf. Sci. Eng. Trans. ASME, 136, 051011, 10.1115/1.4027839 · doi:10.1115/1.4027839
  69. Alpert, 1993, A class of basis in L2 for the sparer representation of integral operators, SIAM J. Math. Anal., 24, 246, 10.1137/0524016 · doi:10.1137/0524016
  70. Goodman, 1994, Wavelet of multipliciry r, Trans. Am. Math. Soc., 338, 639, 10.1090/S0002-9947-1993-1117215-0 · doi:10.1090/s0002-9947-1993-1117215-0
  71. Lebrun, 1998, Balanced multiwavelets theory and design, IEEE Trans. Signal Process., 46, 1119, 10.1109/78.668561 · doi:10.1109/78.668561
  72. Cotronei, 1998, Multiwavelet analysis and signal processing, IEEE Trans. Circuits Syst. II: Analog Digit. Signal Process., 45, 970
  73. Keinert, 2001, Raising multiwavelet approximation order through lifting, SIAM J. Math. Anal., 32, 1032, 10.1137/S0036141098349509 · doi:10.1137/s0036141098349509
  74. Khadem, 2003, Development of vibration signature analysis using multiwavelet systems, J. Sound Vib., 261, 613, 10.1016/S0022-460X(02)00992-6 · doi:10.1016/s0022-460x(02)00992-6
  75. W.J. Li, Y.C. Li, A Method of abrupt sensor fault diagnosis, in: Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), 2006, pp. 1–6. · doi:10.1109/isda.2006.64
  76. Sun, 2012, Multiwavelet denoising using SureBlock thresholding and its application in the gearbox fault diagnosis of the rolling mills, Int. J. Compr. Eng., 2, 22, 10.7763/IJCCE.2012.V1.7 · doi:10.7763/ijcce.2012.v1.7
  77. Sun, 2011, Translation-invariant multiwavelet denoising using improved neighbouring coefficients and its application on rolling bearing fault diagnosis, J. Phys.: Conf. Ser., 305, 01
  78. Sun, 2014, Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold, App. Acoust., 77, 122, 10.1016/j.apacoust.2013.04.016 · doi:10.1016/j.apacoust.2013.04.016
  79. Yang, 2009, Construction of a class of compactly supported symmetric and balanced refinable function vector by GTST, Appl. Math. Comput., 207, 83, 10.1016/j.amc.2007.10.066 · doi:10.1016/j.amc.2007.10.066
  80. Zhou, 2013, On the construction and properties of the multi-scaling function via GCTST, Asia Sci. Technol. Portal, 2013, 1273
  81. Amir, 2007, Multiwavelet frames in signal space originated from Hermite splines, IEEE Trans. Signal Process., 55, 797, 10.1109/TSP.2006.887569 · doi:10.1109/tsp.2006.887569
  82. Chui, 2006, Construction of orthonormal multi-wavelets with additional vanishing moments, Adv. Comput. Math., 24, 239, 10.1007/s10444-004-7610-7 · doi:10.1007/s10444-004-7610-7
  83. Yang, 2006, Raising approximation order of refinable vector by increasing multiplicity, Sci. China Ser. A: Math., 49, 86, 10.1007/s11425-005-0040-2 · doi:10.1007/s11425-005-0040-2
  84. Yuan, 2010, Gear fault detection using customized multiwavelet lifting schemes, Mech. Syst. Signal Process., 24, 1509, 10.1016/j.ymssp.2009.11.003 · doi:10.1016/j.ymssp.2009.11.003
  85. Wang, 2009, Multiwavelet construction via an adaptive symmetric lifting scheme and its applications for rotating machinery fault diagnosis, Meas. Sci. Technol., 20, 1, 10.1088/0957-0233/20/4/045103 · doi:10.1088/0957-0233/20/4/045103
  86. Chen, 2012, Improved spectral kurtosis with adaptive redundant multiwavelet packet and its applications forrotating machinery fault detection, Meas. Sci. Technol., 23, 1, 10.1088/0957-0233/23/4/045608 · doi:10.1088/0957-0233/23/4/045608
  87. Jiang, 2013, Rolling bearing fault detection using an adaptive lifting multiwavelet packet with a 1 1/2 dimension spectrum, Mech. Syst. Signal Process., 24, 1
  88. Chen, 2014, Construction of customized redundant multiwavelet via increasing multiplicity for fault detection of rotating machinery, Mech. Syst. Signal Process., 42, 206, 10.1016/j.ymssp.2013.08.024 · doi:10.1016/j.ymssp.2013.08.024
  89. He, 2014, Incipient-signature identification of mechanical anomalies in a ship-borne satellite antenna system using an ensemble multiwavelet, Meas. Sci. Technol., 25, 105006, 10.1088/0957-0233/25/10/105006 · doi:10.1088/0957-0233/25/10/105006
  90. Chen, 2015, Planetary gearbox condition monitoring of ship-based satellite communication antennas using ensemble multiwavelet analysis method, Mech. Syst. Signal Process., 54–55, 277, 10.1016/j.ymssp.2014.07.026 · doi:10.1016/j.ymssp.2014.07.026
  91. Donald, 2006, Bagged super wavelets reduction for boosted prostate cancer classification of seldi-tof mass spectral serum profiles, Chemom. Intell. Lab. Syst., 82, 2, 10.1016/j.chemolab.2005.08.007 · doi:10.1016/j.chemolab.2005.08.007
  92. Cai, 2001, Incorporating information on neighboring coefficients in wavelet estimation, Sankhya Ser. B, 63, 127
  93. Claypoole, 1998, Adaptive wavelet transforms via lifting, Acoustics, Speech Signal Process., 3, 1513
  94. Chen, 2013, A pseudo wavelet system-based vibration signature extracting method for rotating machinery fault detection, Sci. China Technol. Sci., 56, 1294, 10.1007/s11431-013-5139-z · doi:10.1007/s11431-013-5139-z
  95. H. Chen, J.H. Guo, The application of data-driven TF analysis methods in LFM Signal Parameter Estimation, in: Proceedings of IEEE Region 10 Conference, Xi’an, China, 2013.
  96. Chen, 2014, A data-driven threshold for wavelet sliding window denoising in mechanical fault detection, Sci. China Technol. Sci., 57, 589, 10.1007/s11431-013-5451-7 · doi:10.1007/s11431-013-5451-7

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Metadata

Authors
Chen, J., Li, Z., Pan, J., Chen, G., Zi, Y., Yuan, J., Chen, B., & He, Z.
Venue
Mechanical Systems and Signal Processing
Publisher
Elsevier BV
Published
2016-03
Volume
70-71
Pages
1-35
ISSN
0888-3270 (ISSN-L)
DOI
10.1016/j.ymssp.2015.08.023
OpenAlex
W1438045566
Cited by
517
References
88
FWCI
36.19
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • National Natural Science Foundation of China (51421004, 51405379)
  • China Postdoctoral Science Foundation (2014M562396, 2015T81017)
  • Fundamental Research Funds for the Central Universities of China (XJJ2015106, CXTD2014001)
  • Shaanxi Industrial Science and Technology Project (2015GY121)

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CoChat. (2026). Citation check: Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. https://app.cochat.ai/papers/10.1016/j.ymssp.2015.08.023
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Chen, J., Li, Z., Pan, J., Chen, G., Zi, Y., Yuan, J., Chen, B., & He, Z. (2016). Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2015.08.023

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