Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Advanced Signal Processing for Structural Health Monitoring Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery

verfasst von : Shuilong He, Yikun Liu, Jinglong Chen, Yanyang Zi

Erschienen in: Structural Health Monitoring

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation experiments. Then the newer development of WT based on inner product is introduced. The construction and applications of major developments on adaptive multiwavelet in RMFD are presented. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Holobalancing Method and Its Improvement by Reselection of Balancing Object
Nächstes Kapitel Wavelet Based Spectral Kurtosis and Kurtogram: A Smart and Sparse Characterization of Impulsive Transient Vibration
Literatur
1.
Feng Z.P., Zuo M.J., “Fault diagnosis of planetary gearboxes via torsional vibration signal analysis,” Mechanical Systems and Signal Processing, 2013, 36:401–421.
2.
Li J.M., Chen X.F., He Z.J., “Multi-stable stochastic resonance and its application research on mechanical fault diagnosis,” Journal of Sound and Vibration, 2013, 332(22):5999–6015.
3.
Marquez F., Tobias A., Perez J., et al., “Condition monitoring of wind turbines: techniques and methods,” Renewable Energy, 2012, 46:169–178.
4.
Chen B.Q., Zhang Z.S., Zi Y.Y., et al., “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,” Mechanical Systems and Signal Processing, 2013, 40:1–37.
5.
Sun H.L., He Z.J., Zi Y.Y., et al., “Multiwavelet transform and its applications in mechanical fault diagnosis – A review, Mechanical Systems Signal Processing,” 2014, 43(1–2):1–24.
6.
Qin, S.R., Zhong Y.M., 2004 “Research on the unified mathematical model for FT, STFT and WT and its applications,” Mechanical Systems and Signal Processing, 18 (6):1335–1347.
7.
Nilsen G.K., “Recursive Time-Frequency Reassignment, IEEE Transactions on Signal Processing,” 2009, 57(8):3283–3287.
8.
Liu X.P., Shi J., Sha X.J., et al., “A general framework for sampling and reconstruction in function spaces associated with fractional Fourier transform,” Signal Processing, 2015, 107:319–326.
9.
Giv H.H., “Directional short-time Fourier transform,” Journal of Mathematical Analysis and Applications, 2013, 399(1):100–107.
10.
Baccar D., Söffker D., “Wear detection by means of wavelet-based acoustic emission analysis,” Mechanical Systems Signal Processing, 2015, 60–61:198–207.
11.
Ł. Jedliński, J. Jonak, “Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform,” Applied Soft Computing, 2015, 30: 636–641.
12.
Gao R.X., Yan R.Q., Wavelets: Theory and Applications for Manufacturing, Springer-Verlag, 2011.
13.
Randall R.B., Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications, Wiley, 2011.
14.
Lei Y.G., Lin J., Zuo M. J., et al., “Condition monitoring and fault diagnosis of planetary gearboxes: A review,” Measurement, 2014, 48:292–305.
15.
Li B., Chen X.F., “Wavelet-based numerical analysis: A review and classification.” Finite Elements in Analysis and Design, 2014, 81:14–31.
16.
Sweldens W., “The lifting scheme: A custom design construction of biorthogonal wavelets,” Applied Computational Harmonic Analysis, 1996, 2:186–200.
17.
Li Z., He Z.J., Zi Y.Y., et al., “Rotating machinery fault diagnosis using signal-adapted lifting scheme,” Mechanical Systems Signal Processing, 2008, 22(3): 542–556.
18.
Xiao W.R., Zi Y.Y., Chen B.Q., et al., “A novel approach to machining condition monitoring of deep hole boring,” International Journal of Machine Tools and Manufacture, 2014, 77:27–33.
19.
Kingsbury N.G., “The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters,” IEEE Digital Signal Processing Workshop, 1998.
20.
Donovan G.C., Geronimo J.S., Hardin D.P., et al., “Construction of orthogonal wavelets using fractal interpolation functions,” SIAM J. on Mathematical Analysis, 1996, 27(3):1158–1192.
21.
Zfian, M.H. Moradi, S. Gharibzadeh, “Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms,” Signal Image and Video Processing, 2009, 4:177–185.
22.
Downie T.R., Silverman B.W., “The discrete multiple wavelet transform and thresholding methods,” IEEE Trans. on Signal Processing, 1998, 46:2558–2561.
23.
Peng Z.K., Chu F.L., “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography,” Mechanical Systems Signal Processing, 2004, 18:199–221.
24.
He Z.J., Zi Y.Y., Chen X.F., “Transform principle of inner product for fault diagnosis”. Journal of Vibration Engineering, 2007, 20(5):528–533.
25.
B.Q. Chen, Z.S. Zhang, C. Sun, et al., “Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors,” Mechanical Systems Signal Processing, 2012, 33:275–298.
26.
Mallat S., A Wavelet Tour of Signal Processing, Second Edition, Academic Press, 2003.
27.
Lin J., Qu L.S., “Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis,” Journal of Sound and Vibration, 2000, 234(1):135–148.
28.
Lin J., Zuo M.J., “Gearbox fault diagnosis using adaptive wavelet filter,” Mechanical Systems and Signal Processing, 2003, 17(6):1259–1269.
29.
Grossmann A., Morlet, J., “Decomposition of hardy functions into square integrable wavelets of constant shape,” SIAM Journal on Mathematical Analysis, 1984, 15(4):723–746.
30.
Meyer Y. “Principe d’incertitude, bases hilbertiennes et algebres d’operateurs,” Séminaire Bourbaki, 1986, 662:209–223.
31.
Mallat S., “Multiresolution approximations and wavelet orthonormal bases of L2(R),” Transactions of the American Mathematical Society, 1989, 315(1):69–87.
32.
Mallat S., “A theory for multiresolution signal decomposition - The wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989 11(7):674–693.
33.
Daubechies I., “Orthonormal bases of compactly supported wavelets,” Communications on Pure and Applied Mathematics, 1988, 41(7):909–996.
34.
Daubechies I., “Ten lectures of wavelets,” CBMS-NSF Regional Conference Series in Applied Mathematics. Society for Industrial and Applied Mathematics: Philadelphia, PA, 1992.
35.
Coifman R.R., Meyer Y., Quake S., et al., “Signal processing and compression with wavelet packets,” Wavelets and Their Applications, 1994, 442:363–379.
36.
Li H., Zhang Y., Zheng H., “Application of Hermitian wavelet to crack fault detection in gearbox,” Mechanical Systems and Signal Processing, 2011, 25(4):1353–1363.
37.
Keinert F., Wavelets and Multiwavelets, Studies in Advanced Mathematics, Chapman & Hall/CRC Press, 2004.
38.
Alpert B., “A class of basis in L2 for the sparer representation of integral operators,” SIAM Journal on Mathematical Analysis, 1993, 24 (1):246–262.
39.
Jiang Q.T., “Orthogonal multiwavelet with optimum time-frequency resolution,” IEEE Transactions on Signal Processing, 1998, 46(4):830–844.
40.
Wang X.D., Zi Y.Y., and He Z.J., “Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis,” Mechanical Systems and Signal Processing, 2011, 25:285–304.
41.
Cai T.T., Silverman B.W., “Incorporating information on neighboring coefficients in wavelet estimation,” Sankhya Series B, 2011, 63:127–148.
42.
Chen J.L., Li Z.P., Pan J., Chen G.G., Zi Y.Y., and Z.J. He, “Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review,” Mechanical Systems and Signal Processing, 2016, 70–71:1-35.
Metadaten
Titel
Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery
verfasst von
Shuilong He
Yikun Liu
Jinglong Chen
Yanyang Zi
Copyright-Jahr
2017
Buch
Structural Health Monitoring

Print ISBN: 978-3-319-56125-7

Electronic ISBN: 978-3-319-56126-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-56126-4_4

Neuer Inhalt

    Bildnachweise