The Paton Welding Journal, 2022, №01



2022 №01 (08)

DOI of Article 10.37434/tpwj2022.01.09

2022 №01 (01)

---

The Paton Welding Journal, 2022, #1, 48-58 pages

Methods and means of early vibration diagnostics of rotating components of mechanisms of quay container handlers

I.M. Javorskyi², R.M. Yuzefovych³, O.V. Lychak¹, P.O. Semenov⁴

¹Karpenko Physico-Mechanical Institute of the NASU 5 Naukova Str., 79060, Lviv, Ukraine,
²Politechnika Bydgoska 7 Prof. Sylwestra Kaliskiego, 85796, Bydgoszcz, Poland,
³Lviv Polytechnic National University 12 Stepan Bandera Str., 79013 Lviv, Ukraine,
⁴Odesa National Maritime University 34 I. Mechnikov Str., 65029, Odesa, Ukraine

Abstract
The paper describes the properties of a model of vibration of interconnected rotating mechanisms in the form of biperiodically nonstationary random processes (BPNRP). Individual cases of such a model are considered, which enable performing data analysis by the method of periodically nonstationary random processes (PNRP). These methods are used to analyze the condition of mechanisms at increased vibration level. Separation of deterministic and stochastic vibrations was performed and parameters describing the structure of hidden periodicities of the first and second order were determined. The causes for increased vibration level were established.
Keywords: lifting mechanism, vibration, periodical nonstationarity, deterministic oscillations, amplitude spectrum, stochastic high-frequency modulation, dispersion

Received: 07.12.2021
Accepted: 07.02.2022

References

1. Javorskyj, I.M. (2013) Mathematical models and analysis of stochastic oscillations. Lviv, PMI of NAS of Ukraine [in Ukrainian].
2. Javorskyj, I., Mykhailyshyn, V. (1996) Probabilistic models and statistical analysis of stochastic oscillations. Pattern Recogn. Image. Anal., 6(4), 749-763.
3. Javorskyj, I., Yuzefovych, R., Kravets, I., Matsko, I. (2014) Methods of periodically correlated random processes and their generalizations. Cyclostationarity: Theory and Methods. Lecture Notes in Mechanical Engineering. Ed. by F. Chaari, J. Leskow, A. Sanches-Ramires. New York, Springer Int. Publish. Switzerland, 73-93. https://doi.org/10.1007/978-3-319-04187-2_6
4. Javorskyj, I., Dzeryn, O., Yuzefovych, R. (2019) Analysis of mean function discrete LSM-estimator for biperiodically nonstationary random signals. Math. Model. Comput., 6(1), 44-57. https://doi.org/10.23939/mmc2019.01.044
5. McCormick, A.C., Nandi, A.K. (1998) Cyclostationarity in rotating machine vibrations. Mech. Syst. Signal Process., 12(2), 225-242. https://doi.org/10.1006/mssp.1997.0148
6. Capdessus, C., Sidahmed, M., Lacoume, J.L. (2000) Cyclostationary processes: Application in gear fault early diagnostics. Mech. Syst. Signal Process., 14(3), 371-385. https://doi.org/10.1006/mssp.1999.1260
7. Antoni, J., Bonnardot, F., Raad, A., El Badaoui, M. (2004) Cyclostationary modeling of rotating machine vibration signals. Mech. Syst. Signal Process., 18, 1285-1314. https://doi.org/10.1016/S0888-3270(03)00088-8
8. Antoni, J. (2009) Cyclostationarity by examples. Mech. Syst. Signal Process., 23, 987-1036. https://doi.org/10.1016/j.ymssp.2008.10.010
9. Randall, R.B., Antoni, J. (2011) Rolling element bearing diagnostics - A tutorial. Mech. Syst. Signal Process., 25(2), 485-520. https://doi.org/10.1016/j.ymssp.2010.07.017
10. Zimroz, R., Bartelmus, W. (2009) Gearbox condition estimation using cyclostationary properties of vibration signal. Key Engineering Mater., 413(1), 471-478. https://doi.org/10.4028/www.scientific.net/KEM.413-414.471
11. Hurd, H.L., Miamee, A. (2007) Periodically correlated random sequences: Spectral theory and practice. New York, Wiley. https://doi.org/10.1002/9780470182833
12. Javorskyj, I., Kravets, I., Matsko, I., Yuzefovych, R. (2017) Periodically correlated random processes: Application in early diagnostics of mechanical systems. Mech. Syst. Signal Process., 83, 406-438. https://doi.org/10.1016/j.ymssp.2016.06.022
13. Javorskyj, I., Matsko, I., Yuzefovych, R. et al. (2021) Methods of hidden periodicity discovering for gearbox fault detection. Sensors., 21, 6138. https://doi.org/10.3390/s21186138
14. Javorskyj, I., Leśkow, J., Kravets, I. et al. (2011) Linear filtration methods for statistical analysis of periodically correlated random processes. Pt II: Harmonic series representation. Signal Process., 91, 2506-2519. https://doi.org/10.1016/j.sigpro.2011.04.031
15. Yuzefovych, R., Javorskyj, I., Matsko, I. et al. (2020) Devices for detection of defects at early stages of their initiation at determination of technical condition of mechanisms. Tekh. Diahnost. ta Neruiniv. Kontrol, 4, 8-16 [in Ukrainian]. https://doi.org/10.37434/tdnk2020.04.02
16. Javorskyj, I., Yuzefovych, R., Lychak, O. et al. (2021) Methods and means of early vibrodiagnostics of bearing units of rotary mechanisms. Tekh. Diahnost. ta Neruiniv. Kontrol, 2, 30-37 [in Ukrainian]. https://doi.org/10.37434/tdnk2021.02.04
17. Javorskyj, I., Yuzefovych, R., Matsko, I., Zakrzewski, Z. (2021) The least square estimation of the basic frequency for periodically non-stationary random signals. Digital Signal Process., 116, 103113. 'https://doi.org/10.1016/j.dsp.2021.103113'); ?>
18. Javorskyj, I., Yuzefovych, R., Matsko, I., Kurapov, P. (2021) Hilbert transform of a periodically non-stationary random signal: Low-frequency modulation. Digital Signal Process., 116, 103113. https://doi.org/10.1016/j.dsp.2021.103113

---