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Application of time-frequency distributions in diagnostic signal processing problems: a case study

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PL
Zastosowanie dystrybucji czasowo-częstotliwościowych w diagnostycznym przetwarzaniu sygnałów: studium przypadku
Języki publikacji
EN
Abstrakty
EN
In this paper, the author analyzed an applicability of selected types of time-frequency distributions that belong to Cohen’s class and their reassignments for signals similar to those obtained during machinery diagnostics. At the first step of performed studies a synthetic multicomponent signal that contains both stationary and non-stationary components was analyzed using algorithms based on various time-frequency distributions. This allows for evaluating effectiveness of identification of particular components by applied time-frequency distributions and selecting a group of the most effective algorithms. At the second step, the selected time-frequency distributions were applied for analysis of signals acquired during diagnosis of rolling bearings in order to verify the effectiveness of identification of components responsible for a priori known faults occurred in bearings.
PL
W niniejszym artykule autor analizuje stosowalność wybranych typów dystrybucji czasowoczęstotliwościowych, które należą do klasy Cohena i ich wersji redefiniowanych dla sygnałów zbliżonych do takich, które są otrzymywane podczas diagnostyki maszyn. W pierwszym kroku przeprowadzonych badań syntetyczny wieloskładowy sygnał, zawierający zarówno stacjonarne jak i niestacjonarne składowe, był analizowany z wykorzystaniem algorytmów opartych na różnych dystrybucjach czasowoczęstotliwościowych. Pozwoliło to na ocenę efektywności identyfikacji poszczególnych składowych przez zastosowane dystrybucje czasowo-częstotliwościowe oraz wybór grupy najefektywniejszych algorytmów. W drugim kroku wybrane dystrybucje czasowo-częstotliwościowe zostały zastosowane do analizy sygnałów pozyskanych podczas diagnostyki łożysk tocznych w celu weryfikacji efektywności identyfikacji składowych odpowiedzialnych za wystąpienie uszkodzeń w łożyskach, znanych a priori.
Czasopismo
Rocznik
Strony
95--103
Opis fizyczny
Bibliogr. 35 poz., rys.
Twórcy
autor
  • Institute of Fundamentals of Machinery Design, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland
Bibliografia
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  • 5. Mallat S.G. A Wavelet Tour of Signal Processing: The Sparse Way. San Diego, CA: Academic Press, 2009.
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  • 8. Staszewski W.J., Worden K., Tomlinson G.R. Time-frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition. Mechanical Systems and Signal Processing, 1997; 11:673-692. http://dx.doi.org/10.1006/mssp.1997.0102.
  • 9. Bartelmus W., Zimroz R. Vibration condition monitoring of planetary gearbox under varying external load. Mechanical Systems and Signal Processing, 2009; 23:246-257. http://dx.doi.org/10.1016/j.ymssp.2008.03.016.
  • 10. Climente-Alarcon V., Antonino-Daviu J.A., Riera-Guasp M., Puche-Panadero R., Escobar L. Application of the Wigner-Ville distribution for the detection of rotor asymmetries and eccentricity through high-order harmonics. Electric Power Systems Research, 2012; 91:28-36. http://dx.doi.org/10.1016/j.epsr.2012.05.001.
  • 11. Kim Y.B., Kim S.J., Chung H.D., Park Y.W., Park J.H. A study on technique to estimate impact location of loose part using Wigner-Ville distribution. Progress in Nuclear Energy, 2003; 43:261-266. http://dx.doi.org/10.1016/S0149-1970(03)00033-7.
  • 12. Zhu Y.M., Goutte R., Amiel M. On the use of twodimensional Wigner-Ville distribution for texture segmentation. Signal Processing, 1993; 30:329-353. http://dx.doi.org/10.1016/0165-1684(93)90016-4.
  • 13. Yandong L., Xiaodong Z. Wigner-Ville distribution and its application in seismic attenuation estimation. Applied Geophysics, 2007; 4:245-254. http://dx.doi.org/10.1007/s11770-007-0034-7.
  • 14. Urbanek J., Barszcz T., Zimroz R., Antoni J. Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions. Measurement, 2012, 45:1782-1791. http://dx.doi.org/10.1016/j.measurement.2012.04.006.
  • 15. Hassanpour H. A time-frequency approach for noise reduction. Digital Signal Processing, 2008; 18:728-738. http://dx.doi.org/10.1016/j.dsp.2007.09.014.
  • 16. Baydar N., Ball A. A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. Mechanical Systems and Signal Processing, 2001, 15:1091-1107. http://dx.doi.org/10.1006/mssp.2000.1338.
  • 17. Ma J., Wu J., Yuan X. The fault diagnosis of the rolling bearing based on the LMD and time-frequency analysis. International Journal of Control and Automation, 2013; 6:357-376.
  • 18. Antoniadou I., Dervilis N., Papatheou E., Maguire A.E., Worden K. Aspects of structural health and condition monitoring of offshore wind turbines. Philosophical Transactions of the Royal Society A, 2015; 373:20140075. http://dx.doi.org/10.1098/rsta.2014.0075.
  • 19. Stanković L.J., Böhme J.F. Time-frequency analysis of multiple resonances in combustion engine signals. Signal Processing, 1999; 79:15-28. http://dx.doi.org/10.1016/S0165-1684(99)00077-8.
  • 20. Cheng J.Y., Hsieh C.T., Huang S.J., Huang C.M. Application of modified Wigner distribution method to voltage flicker-generated signal studies. International Journal of Electric Power and Energy Systems, 2013; 44:275–281. http://dx.doi.org/10.1016/j.ijepes.2012.07.045.
  • 21. Xu L.Q., Hu L.Q., Chen K.Y., Li E.Z. Time-frequency analysis of nonstationary complex magneto-hydro-dynamics in fusion plasma signals using the Choi-Williams distribution. Fusion Engineering and Design, 2013; 88:2767-2772. http://dx.doi.org/10.1016/j.fusengdes.2013.04.017.
  • 22. Jones D.L., Parks T.W. A resolution comparison of several time-frequency representations. IEEE Transactions on Signal Processing, 1992; 40:413-420. http://dx.doi.org/10.1109/ICASSP.1989.266906.
  • 23. Hlawatsch F., Manickam T.G., Urbanke R.L., Jones W. Smoothed pseudo-Wigner distribution, Choi-Williams distribution, and cone-kernel representation: Ambiguity-domain analysis and experimental comparison. Signal Processing, 1995; 43:149-168. http://dx.doi.org/10.1016/0165-1684(94)00150-X.
  • 24. Hammond J.K., White P.R. The analysis of nonstationary signals using time-frequency methods. Journal of Sound and Vibration, 1996; 190:419-447. http://dx.doi.org/10.1006/jsvi.1996.0072.
  • 25. Ma N., Vray D., Delachartre P., Gimenez G. Time-frequency representation of multicomponent chirp signals. Signal Processing, 1997; 56:149-155. http://dx.doi.org/10.1016/S0165-1684(96)00163-6.
  • 26. Wang Y., Jiang Y. Generalized time-frequency distributions for multicomponent polynomial phase signals. Signal Processing, 2008; 88:984-1001. http://dx.doi.org/10.1016/j.sigpro.2007.10.016.
  • 27. Sejdić E., Djurović I., Jiang J. Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processing, 2009; 19:153-183. http://dx.doi.org/10.1016/j.dsp.2007.12.004.
  • 28. Hansson-Sandsten M. Multitaper Wigner and Choi-Williams distributions with predetermined Dopplerlag bandwidth and sidelobe suppression. Signal Processing, 2011; 91:1457-1465. http://dx.doi.org/10.1016/j.sigpro.2010.10.010.
  • 29. Randall R.B., Antoni J. Rolling element bearing diagnostics – A tutorial. Mechanical Systems and Signal Processing, 2011; 25:485-520. http://dx.doi.org/10.1016/j.ymssp.2010.07.017.
  • 30. Cohen L. Time-frequency distributions – A review. Proceedings of IEEE, 1989; 77:941-981. http://dx.doi.org/10.1109/5.30749.
  • 31. Papanderou A., Faye Boudreaux-Bartels G. Distributions for time-frequency analysis: a generalization of Choi-Williams and Butterworth distribution. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, San Francisco, CA, 1992; 5:181-184. http://dx.doi.org/10.1109/ICASSP.1992.226628.
  • 32. Flandrin P., Martin W. A general class of estimators for the Wigner-Ville spectrum of nonstationary processes. Bensoussan A., Lions J.L., Eds., Systems Analysis and Optimization of Systems, Lecture Notes in Control and Information Sciences, 1984; 62:15-23. http://dx.doi.org/10.1007/BFb0004941.
  • 33. Auger F., Flandrin P. Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Transactions on Signal Processing, 1995; 43:1068-1089. http://dx.doi.org/10.1109/78.382394.
  • 34. Auger F., Flandrin P., Gonçalvès P., Lemoine O. Time-Frequency Toolbox. CNRS France, Rice University, 1996, http://tftb.nongnu.org.
  • 35. Katunin A., Wysogląd B., Gawron V. Conditio monitoring of roller bearings based on estimation of Hurst exponents of vibration signals. Scientific Problems of Machines Operation and Maintenance, 2012; 47: 51-62.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3d46e7a1-376a-4d02-afce-5818b6dbda17
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