"Tekhnichna Diahnostyka ta Neruinivnyi Kontrol" (Technical Diagnostics and Non-Destructive Testing) #2, 2023, pp. 17-21
Selection and analysis of the deterministic component of vibrations by the least squares method
R.M. Yuzefovych2, I.M. Javorskyj3, O.V. Lychak1, V.V. Gnatyshyn2, M.Z. Varyvoda1
1G.V. Karpenko Physico-Mechanical Institute of NASU. 5 Naukova str., 79060, Lviv, Ukraine. Е-mail: roman.yuzefovych@gmail.com
2Lviv Polytechnic National University. 12 S. Bandery str., 79000, Lviv, Ukraine.
3Bydgoszcz University of Sciences and Technology. 7, Prof. S. Kaliskiego al., 85796, Bydgoszcz, Poland.
The results of investigations by the least squares method of the mathematical expectation of periodically nonstationary random
processes as a mathematical model of stochastic vibrations are considered. Analysis of the dependencies which determine the
statistical characteristics of evaluation was performed. Examples of analysis of typical processes are given. 20 Ref., 2 Fig.
Keywords: periodically correlated random processes (PCRP), vibration, mathematical expectation, correlation function,
evaluation by the least squares method, dispersion
Received: 28.04.2023
References
1. Javorskyj, I.M. (2013) Mathematical models and analysis of stochastic oscillations. Lviv, PMI [in Ukrainian].
2. Hurd, H.L. (1991) Correlation theory of almost periodically correlated processes. J. Multivariate Anal., 37, 24-45.
https://doi.org/10.1016/0047-259X(91)90109-F3. Matsko, I., Javorskyj, I., Isaev, I. et al. (2009) Methods for enhancement of the efficiency of statistical analysis of vibration signals from the bearing supports of turbines at thermal-electric power plants. Mater. Sci., 45(3), 378-391.
https://doi.org/10.1007/s11003-009-9202-74. Javorskyj, I., Kravets, I., Matsko, I., Yuzefovych, R. (2017) Periodically correlated random processes: application in early diagnostics of mechanical systems. Mech. Syst. and Sign. Process., 83, 406-438.
https://doi.org/10.1016/j.ymssp.2016.06.0225. Javorskyj, I., Matsko, I., Yuzefovych, R. et al. (2021) Methods of Hidden Periodicity Discovering for Gearbox Fault Detection. Sensors, 21(18), 6138.
https://doi.org/10.3390/s211861386. McCormick, A.C.; Nandi, A.K. (1998) Cyclostationarity in rotating machine vibrations. Mech. Syst. and Sign. Process., 12(2), 225-242.
https://doi.org/10.1006/mssp.1997.01487. Capdessus, C., Sidahmed, M., Lacoume, J.L. (2000) Cyclostationary processes: application in gear faults early diagnosis. Mech. Syst. and Sign. Process., 14(3), 371-385.
https://doi.org/10.1006/mssp.1999.12608. Dalpiaz, G., Rivola, A., Rubini, R. (2000) Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears. Mech. Syst. and Sign. Process., 14(3), 387-412.
https://doi.org/10.1006/mssp.1999.12949. Bouillout, L., Sidahmed, M. (2001) Cyclostationary approach and bilinear approach: comparison, applications to early diagnostics for helicopter gearbox and classification method based on HOCS. Mech. Syst. and Sign. Process., 15(5), 923-943.
https://doi.org/10.1006/mssp.2001.141210. Antoniadis, I., Glossiotis, G. (2001) Cyclostationary analysis of rolling element bearing vibration signals. J. Sound Vib., 248(5), 829-845.
https://doi.org/10.1006/jsvi.2001.381511. Antoni, J., Bonnardot, F., Raad, A., El Badaoui, M. (2004) Cyclostatinary modeling of rotating machine vibration signals. Mech. Syst. and Sign. Process., 18, 1285-1314.
https://doi.org/10.1016/S0888-3270(03)00088-812. Li, L., Qu, L. (2003) Cyclic statistics in rolling bearing diagnosis. J. Sound Vib., 267(2), 253-265.
https://doi.org/10.1016/S0022-460X(02)01412-813. Zhu, Z., Kong, F. (2005) Cyclostationary analysis for gearbox condition monitoring: approaches and effectiveness. Mech. Syst. and Sign. Process., 19(3), 467-482.
https://doi.org/10.1016/j.ymssp.2004.02.00714. (1994) Cyclostationarity in Communications and Signal Processing. Ed. by W.A. Gardner. IEEE Press, New York.
15. Gardner, W.A. (1985) Introduction to Random Processes with Application to Signals and Systems. New York, Macmillan.
16. Hurd, H.L., Miamee, A. (2007) Periodically Сorrelated Random Sequences. Spectral Theory and Practice. Wiley-Interscience, New Jersey.
https://doi.org/10.1002/978047018283317. Dehay, D., Hurd, H.L. (1994) Representation and estimation for periodically and almost periodically correlated random processes. Cyclostationarity in Communications and Signal Processing. IEEE Press, New York, 295-326.
18. Antoni, J. (2009) Cyclostationarity by examples. Mech. Syst. and Sign. Process., 23(4), 987-1036.
https://doi.org/10.1016/j.ymssp.2008.10.01019. Randall, R.B., Antoni, J. (2011) Rolling element bearing diagnostics - A tutorial. Mech. Syst. and Sign. Process., 25(2), 485-520.
https://doi.org/10.1016/j.ymssp.2010.07.01720. Javorskyj, I., Yuzefovych, R., Matsko, I., Zakrzewski, Z. (2022) The least square estimation of the basic frequency for periodically non-stationary random signals. Digit. Signal Process., 122, 103333.
https://doi.org/10.1016/j.dsp.2021.103333
Advertising in this issue: