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Comparison of advanced signal-processing methods for roller bearing faults detection

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Wind turbines are nowadays one of the most promising energy sources. Every year, the amount of energy produced from the wind grows steadily. Investors demand turbine manufacturers to produce bigger, more efficient and robust units. These requirements resulted in fast development of condition-monitoring methods. However, significant sizes and varying operational conditions can make diagnostics of the wind turbines very challenging. The paper shows the case study of a wind turbine that had suffered a serious rolling element bearing (REB) fault. The authors compare several methods for early detection of symptoms of the failure. The paper compares standard methods based on spectral analysis and a number of novel methods based on narrowband envelope analysis, kurtosis and cyclostationarity approach. The very important problem of proper configuration of the methods is addressed as well. It is well known that every method requires setting of several parameters. In the industrial practice, configuration should be as standard and simple as possible. The paper discusses configuration parameters of investigated methods and their sensitivity to configuration uncertainties.
Rocznik
Strony
715--726
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
autor
autor
autor
  • AGH University of Science and Technology, Faculty of mechanical Engineering and robotics, Al. Mickiewicza 30, 30-059 Kraków, Poland, urbanek@agh.edu.pl
Bibliografia
  • [1] Zoltowski, B., Cempel, C. (2004). Engineering of Machinery Diagnostics PTDT ITE PIB Radom. Warszawa, Bydgoszcz, Radom. (in Polish)
  • [2] Klein, U., (2003). Schwingungsdiagnostische Beurteilung von Maschinen und Anlagen (Vibrodiagnostic assessment of machines and devices). Stahleisen Verlag, Duesseldorf.
  • [3] Taylor, T. (2000). The bearing analysis handbook. Vibration Consultants Inc.
  • [4] Antoni, J. (2009). Cyclostationarity by examples. Mechanical Systems and Signal Processing, 23, 987-1036.
  • [5] Randall, R.B., Antoni, J. (2011) Rolling element bearing diagnostics - A. tutorial. Mechanical Systems and Signal Processing, 25, 485-520.
  • [6] Urbanek, J., Antoni, J., Barszcz, T. (2012). Detection of signal component modulations using modulation intensity distribution. Mechanical Systems and Signal Processing, 28, 399-413.
  • [7] Davis, F.B. (1996). Advanced vibration analysis techniques for fault detection and diagnosis in geared transmission systems. PhD thesis. Swinburn University of Technology, Australia.
  • [8] Urbanek, J., Barszcz, T., Zimroz, R., Antoni, J. (2012). Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions. Measurement 45, 1782-1791.
  • [9] Barszcz, T. (2009). Selection of diagnostic algorithms for wind turbines. Diagnostyka, 50, 7-12.
  • [10] Zimroz, R., Barszcz, T., Urbanek, J., Bartelmus, W., Millioz, F., Martin, N. (2011). Measurement Of Instantaneous Shaft Speed By Advanced Vibration Signal Processing - Application To Wind Turbine Gearbox. Metrol. Meas. Syst.,18(4), 701-712.
  • [11] Urbanek, J., Barszcz, T., Sawalhi, N., Randall, R.B. (2011). Comparison of amplitude based and phase based methods for speed tracking in application to wind turbines. Metrol. Meas.Syst., 18(2), 295-304.
  • [12] Gellermann, T. (2003). Requirements for Condition Monitoring Systems for Wind Turbines, AZT Expertentage,. Allianz
  • [13] Hau, E. (2006). Wind Turbines. Springer, Berlin.
  • [14] Zimroz, R, (2009). Some remarks on local damage diagnosis in presence of multi-faults and non-stationary operation. The Sixth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Dublin, Ireland, The British Institute of Non-Destructive Testing, US Society for Machinery Failure Prevention Technology, 33-44.
  • [15] Ebner, R., Gruber, P., Ecker, W., Kolednik, O., Krobath, M., Jesner, G. (2010). Fatigue damage mechanisms and damage evolution near cyclically loaded edges. Bull. Pol. Acad. Sci.-Tech. Sci., 58(2).
  • [16] Antoni, J. (2007). Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, 21, 108-124.
  • [17] Antoni, J. (2006). The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 20, 282-307.
  • [18] Antoni, J., Randall, R.B. (2006). The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, 20, 308-331.
  • [19] Halang, W.A., Śnieżek, M. (2010). A safe programmable electronic system. Bull. Pol. Acad. Sci.-Tech. Sci., 58(3).
  • [20] Słowik, A. (2011). Application of evolutionary algorithm to design minimal phase digital filters with nonstandard amplitude characteristics and finite bit word length. Bull. Pol. Acad. Sci.-Tech. Sci., 59(2).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0106-0008
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