Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This paper presents the possibility of using the dispersion entropy with a sliding window to assess the stability of machine operation. Attention was focused on the feasibility of using a sliding window and the assessment of the minimum length of the window that produces stable results. The answer to this question is open to all and depends on the complexity of the physics of the phenomenon. The research was carried out first for simple mechanical systems, then for non-linear systems, and then, in the final part of the research, attention was paid to the real signals describing the displacement of the pan in the bearing. These studies are important in determining the minimum window length to conclude the diagnosis of mechanical systems; the narrower the window, not only reduces the need for computing power but above all allows a faster response.
Czasopismo
Rocznik
Tom
Strony
art. no. 2024415
Opis fizyczny
Bibliogr. 24 poz., rys.
Twórcy
autor
- AGH University of Krakow. Poland
autor
- AGH University of Krakow. Poland
- Institute of Fluid Flow Machinery, Polish Academy of Sciences. Gdańsk, Poland
Bibliografia
- 1. Mobley RK. Benefits of predictive maintenance. An Introduction to Predictive Maintenance. 2002;60-73. https://doi.org/10.1016/B978-075067531-4/50004-X.
- 2. Tavner PJ. Review of condition monitoring of rotating electrical machines. IET Electric Power Applications. 2008;2(4):215. https://doi.org/10.1049/iet-epa:20070280.
- 3. Mourtzis D, Vlachou E, Milas N. Industrial Big Data as a Result of IoT Adoption in Manufacturing. Procedia CIRP. 2016; 55: 290-5. https://doi.org/10.1016/j.procir.2016.07.038.
- 4. Pincus SM. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences. 1991;88(6):2297-301. https://doi.org/10.1073/pnas.88.6.2297.
- 5. Lee J, Lapira E, Bagheri B, Kao H an. Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters. 2013; 1(1): 38-41. https://doi.org/10.1016/j.mfglet.2013.09.005.
- 6. Ziółkowski P, Drosińska-Komor M, Głuch J, Breńkacz Ł. Review of methods for diagnosing the degradation process in power units cooperating with renewable energy sources using artificial intelligence. Energies. 2023;16(17):6107. https://doi.org/10.3390/en16176107.
- 7. García Márquez FP, Tobias AM, Pinar Pérez JM, Papaelias M. Condition monitoring of wind turbines: Techniques and methods. Renewable Energy. 2012; 46:169-78. https://doi.org/10.1016/j.renene.2012.03.003.
- 8. Breńkacz Ł, Żywica G. Comparison of experimentally and numerically determined dynamic coefficients of the hydrodynamic slide bearings operating in the nonlinear rotating system. In Proceedings of the Volume 7A: Structures and Dynamics; American Society of Mechanical Engineers. 2017;7A:1-12.
- 9. Żywica G, Breńkacz Ł, Bagiński P. Interactions in the rotor-bearings-support structure system of the multistage ORC microturbine. Journal of Vibration Engineering & Technologies 2018;6(5):369-77. https://doi.org/10.1007/s42417-018-0051-2.
- 10. Randall RB. Vibration-based condition monitoring: industrial. Automotive and Aerospace Applications. Wiley. 2011.
- 11. Tandon N, Choudhury A. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International 1999;32(8):469-80. https://doi.org/10.1016/S0301-679X(99)00077-8.
- 12. Breńkacz Ł. The experimental identification of the dynamic coefficients of two hydrodynamic journal bearings operating at constant rotational speed and under nonlinear conditions. Polish Maritime Research 2017;24(4):108-15. https://doi.org/10.1515/pomr-2017-0142.
- 13. Breńkacz Ł. the experimental identification of the dynamic coefficients of two hydrodynamic journal bearings operating at constant rotational speed and under nonlinear conditions. Polish Marit. Res. 2017;24:108-115. https://doi:10.1515/pomr-2017-0142.
- 14. Breńkacz Ł, Żywica G. The sensitivity analysis of the method for identification of bearing dynamic coefficients. dynamical systems. Modelling. 2015; Springer International Publishing 2016;81-96.
- 15. Breńkacz Ł. Bearing dynamic coefficients in rotordynamics. 1st ed. Wiley .2021.
- 16. Antoniadou I, Manson G, Staszewski WJ, Barszcz T, Worden K. A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions. Mech Syst Signal Process. 2015;64-65:188-216. https://doi.org/10.1016/j.ymssp.2015.03.003.
- 17. Lu C, Wang ZY, Qin WL, Ma J. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing. 2017;130:377-88. https://doi.org/10.1016/j.sigpro.2016.07.028.
- 18. Blaut J, Breńkacz Ł. Application of the TeagerKaiser energy operator in diagnostics of a hydrodynamic bearing. Eksploatacja i Niezawodność - Maintenance and Reliability. 2020;22(4):757-65. https://doi.org/10.17531/ein.2020.4.20.
- 19. Rostaghi M, Azami H. Dispersion entropy: A measure for time-series analysis. IEEE Signal Processing Letters. 2016;23(5):610-4. https://doi.org/10.1109/LSP.2016.2542881.
- 20. Rostaghi M, Ashory MR, Azami H. Application of dispersion entropy to status characterization of rotary machines. Journal of Sound and Vibration. 2019; 438:291-308. https://doi.org/10.1016/j.jsv.2018.08.025.
- 21. Case Western Reserve University Bearing Data Center. Bearing fault data for condition monitoring of bearings. Accessed. [09,2024]. https://engineering.case.edu/bearingdata center.
- 22. Mobley RK. Benefits of predictive maintenance. in an introduction to predictive maintenance. Elsevier. 2002;2:60-73.
- 23. Bandt C, Pompe B. Permutation Entropy: A natural complexity measure for time series. Physical Review Letters. 2002;88(17):174102. https://doi.org/10.1103/PhysRevLett.88.174102.
- 24. Sandoval D, Leturiondo U, Vidal Y, Pozo F. Entropy indicators: An approach for low-speed bearing diagnosis. Sensors. 2021;21(3):849. https://doi.org/10.3390/s21030849.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8d2de097-93e9-4228-ab3f-1999d0417f97