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2020 №04 (01) DOI of Article
10.37434/tdnk2020.04.02
2020 №04 (03)

Technical Diagnostics and Non-Destructive Testing 2020 #04
Technical Diagnostics and Non-Destructive Testing #4, 2020, pp. 8-16

Devices for detection of defects at early stages of their initiation at determination of technical condition of mechanisms

R.M. Yuzefovych, I.M. Yavorskyi, I.Y. Matsko, O.V. Lychak, G.R. Trokym, O.Yu. Dzeryn, I.H. Stetsko


G.V. Karpenko Physico-Mechanical Institute of NASU. 5 Naukova str., 79060, Lviv, Ukraine. E-mail: roman.yuzefovych@gmail.com

Vibration signal is the carrier of information about certain system defects, it has the properties of repeatability and stochasticity. These properties allow describing and studying the mathematical model in the form of periodically correlated random process (PCRP). PCRP probabilistic characteristics reflect the modulation interaction of the stochastic and deterministic components of vibration, which arises in the case of defect appearance. Mutual PCRP-analysis of vibration signals, the use of the introduced coherence functions allows detecting defects, classifying their types, as well as determining their location. The combination of multi-point selection of vibration signals, methods of mutual statistical PCVP-analysis and digital signal processing software in the developed compact device for non-destructive testing «Compact-Vibro» allows increasing the efficiency of vibration diagnostics of rotating units of technological facilities during operation without changing their standard operating modes. The monitoring of TPP turbounits by the developed methods allowed revealing a number of typical defects of support slide bearings, which was confirmed during the repair of rotating units. 20 Ref., 17 Tables.
Keywords: nondestructive testing, vibration, periodically correlated random process, specialized devices, defect, slide bearing

Received: 23.10.2020

References

1. Vogl, G.W., Weiss, B.A., Donmez, M.A.(2014) NISTIR 8012 Standards Related to Prognostics and Health Management (PHM) for Manufacturing: National Institute of Standards and Technology U.S. Department of Commerce. https://doi.org/10.6028/NIST.IR.8012
2. Kollakot, R.A. (1980) Diagnostics of mechanical equipment. Leningrad, Sudostroenie [in Russian].
3. Mygushchenko, R.P. (2014) Elements of control and diagnostics of state of vibration objects: Monography. Kharkiv, Pidruchnyk NTU KhPI [in Ukrainian].
4. 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
5. Capdessus, C., Sidahmed, M., Lacoume, J.L. (2000) Cyclostationary processes: application in gear faults early diagnosis. Ibid, 14 (3), 371-385. https://doi.org/10.1006/mssp.1999.1260
6. Marchenko, B.G., Myslovich, M.V. (1992) Vibration diagnostics of bearing assemblies of electric machines. Kiev: Naukova Dumka [in Russian].
7. (2001) Fracture mechanics and strength of materials: Refer. book. Vol.5: Nondestructive testing and technical diagnostics. Ed. by Z.T. Nazarchuk. Lviv: PMI [in Ukrainian].
8. Yavlensky, K.N., Yavlensky, A.K. (1983) Vibration diagnostics and prediction of quality of mechanical systems. Leningrad, Mashinostroenie [in Russian].
9. Yavorskyi, I.M. (2013) Mathematical models and analysis of stochastic oscillations. Lviv: PMI [in Ukrainian].
10. Javorskyj, I., Yuzefovych, R., Matsko, I., Zakrzewskyi, Z. (2017) Component-wise coherence function for jointly related periodically non-stationary random processes. Radioelectronics and Communication Systems, 60(1), 28-41. https://doi.org/10.3103/S0735272717010046
11. Yuzefovych, R.M., Yavorskyi, I.M., Dzeryn, O.Yu., Trokhym, G.R., Stetsko, I.H., Matsko, I.Y. (2020) Application of specialized nondestructive testing device for analysis of vibration signals of bearing assemblies by the methods of mutual nonstationary analysis. Tekh. Diahnost. ta Neruiniv. Kontrol, 1, 17-27 [in Ukrainian]. https://doi.org/10.37434/tdnk2020.01.02
12. Bendat, J. S., Piersol, A.G. (2010) Random Data : Analysis and Measurement Procedures. New York: John Wiley&Sons. https://doi.org/10.1002/9781118032428
13. Obuchowski, J., Wylomanska, A. Zimroz, R. (2015) Identification of cyclic components in presence of non-Gaussian noise application to crusher bearings damage detection. J. of Vibroengineering, 17 (3), 1242-1252.
14. Hinich, M.J. (2000) A statistical theory of signal coherence. IEEE J. of Oceanic Engineering, 25 (2), 254-259. https://doi.org/10.1109/48.838988
15. Rice, J.A., Rosenblatt, M. (1988) On frequency estimation. Biometrika, 75 (3), 477-484. https://doi.org/10.1093/biomet/75.3.477
16. Bentkus, R., Sushinskas, Yu. (1985) Identification of latent periodicity. Primenenie Teorii Veroyatnostej i Matem. Statistiki, 6, 77-78 [in Russian].
17. Golubev, G.K. (1988) About estimation of the period of unknown shape signal against the white noise background. Problemy Peredachi Informatsii, 29 (4), 38-52 [in Russian].
18. Kulikov, E.I., Trifonov, A.P. (1978) Estimation of signal parameters against the noise background. Moscow, Sov. Radio [in Russian].
19. Javorskyj, І.М., Dzeryn, O.Yu., Yuzefovych, R.M. (2019) Analysis of mean function discrete LSM-estimator for biperiodically nonstationary random signal. Mathematical Modeling and Computing, 6 (1), 44-57. https://doi.org/10.23939/mmc2019.01.044
20. Javorskyj, І.М., Dzeryn, O.Yu., Yuzefovych, R.M. (2020) Discrete LS estimates of correlation function of bi-periodically correlated random signals. Radioelectronics and Communications Systems , 63 (3), 136-155. https://doi.org/10.3103/S0735272720030036

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