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Identification of Weibull Distribution Parameters in the Presence of Noise

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Wear and tear processes, in combination with the dynamics of machines, are the source of many methods of technical objects diagnosis which are useful in practice. Unfortunately, generation of signals is inherently associated with generation of noise and disturbances, which makes the tasks of defining the symptoms and extraction of diagnostic information much more difficult. The article presents a proposal of implementation of a solution eliminating the noise while using the blind equalization method, while also presenting the influence that use of this method has influence on selected reliability characteristics.
Rocznik
Strony
41--52
Opis fizyczny
Bibliogr. 15 poz., wykr.
Twórcy
autor
  • Warsaw University of Technology, Institute of Vehicles
autor
  • Warsaw University of Technology, Institute of Vehicles
Bibliografia
  • 1. Antoni, J. (2005). Blind separation of vibration components: Principles and demonstrations. Mechanical Systems and Signal Processing, 19(6):1166–1180.
  • 2. Drewniak, J. and Hojdys, L. (2015). The method of analysis of fatigue crack growth by Bogdanow-Kozin model. Machine Dynamics Research, 39(4):125–132.
  • 3. Dumont, S., Lebon, F., and Rizzoni, R. (2013). Modeling of stiff interfaces: from statics to dynamics. Machine Dynamics Research, 37(1):35–46.
  • 4. Jakubiak, A. (2013). Probabilistyczne metody detekcji sygnałów na tle zakłóceń. Oficyna Wydawnicza Politechniki Warszawskiej.
  • 5. Jasinski, M. and Radkowski, S. (2013). Detection of tooth crack nucleation using bispectral measures. Machine Dynamics Research, 37:53–60.
  • 6. Jelonnek, B. and Kammeyer, K.-D. (1994). A closed-form solution to blind equalization. Signal Processing, 36(3):251–259.
  • 7. Lee, J.-Y. and Nandi, A. (2000). Extraction of impacting signals using blind deconvolution. Journal of Sound and Vibration, 232(5):945–962.
  • 8. Liu, X. and Randall, R. (2005). Blind source separation of internal combustion engine piston slap from other measured vibration signals. Mechanical Systems and Signal Processing, 19(6):1196–1208.
  • 9. Peled, R., Braun, S., and Zacksenhouse, M. (2005). A blind deconvolution separation of multiple sources, with application to bearing diagnostics. Mechanical Systems and Signal Processing, 19(6):1181–1195.
  • 10. Shalvi, O. and Weinstein, E. (1993). Super-exponential methods for blind deconvolution. IEEE Transactions on Information Theory, 39(2):504–519.
  • 11. Smith, D. J. (2011). Reliability, maintainability and risk: Practical methods for engineers. Elsevier.
  • 12. Tse, P., Gontarz, S., and Wang, X. (2007). Enhanced eigenvector algorithm for recovering multiple sources of vibration signals in machine fault diagnosis. Mechanical Systems and Signal Processing, 21(7):2794–2813.
  • 13. Tse, P. W., Zhang, J., and Wang, X. (2006). Blind source separation and blind equalization algorithms for mechanical signal separation and identification. Journal of Vibration and Control, 12(4):395–423.
  • 14. Wang, D. and Peter, W. T. (2012). A new blind fault component separation algorithm for a single-channel mechanical signal mixture. Journal of Sound and Vibration, 331(22):4956–4970.
  • 15. Zhang, J. and Tse, P. (2003). Detection of incipient impulsive fault by blind equalization. Proceedings of IMS2003, Xi’an, China, pages 25–27.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2a9717d2-d6e8-421f-b223-bf7b69da2863
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