Rationale for the use of vibration signal decomposition as a diagnostic method for rolling bearings of oil pumps
https://doi.org/10.31660/0445-0108-2019-4-122-129
Abstract
Today, the development of new technologies and their application in the oil industry is a key factor in improving the reliability of technological equipment. A promising method of technical diagnostics, described in the article, is intended to identify such defects and is relevant in the consideration of this issue. The data obtained indicate that it is possible not only to isolate the pulse of interest from the signal of the untreated vibration, but also to identify the type of damage in the early stages.
About the Authors
Р. S. Kononov
Industrial University of Tyumen
Russian Federation
Russian Federation
Pavel S. Kononov, Student
Tyumen
Е. V. Ogudova
Industrial University of Tyumen
Russian Federation
Russian Federation
Evgenia V. Ogudova, Senior Lecturer at the Department of Transportation of Hydro-carbon Resources
Tyumen
References
1. Karimi H., & Nasab, H. S. (2012). Diagnosis using acoustic emission. World Pumps, (1), pp. 33-37. (In English). Available at: https://doi.org/10.1016/S0262-1762(11)70400-X
2. Lu, Y., Wang, F., Jia, M., & Qi, Y. (2016). Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters. Mechanical Systems and Signal Processing, 81, pp. 259-273. (In English). Available at: https://doi.org/10.1016/j.ymssp.2016.03.018
3. Wang, J., He, Q. B., & Kong, F. R. (2015). Multiscale envelope manifold for enhanced fault diagnosis of rotating machines. Mechanical Systems and Signal Processing, 52-53, pp. 376-392. (In English). Available at: https://doi.org/10.1016/j.ymssp.2014.07.021
4. Dybala, J. (2018). Diagnosing of rolling-element bearings using amplitude level-based decomposition of machine vibration signal. Measurement, 126, pp. 143-155. (In English). Available at: https://doi.org/10.1016/j.measurement.2018.05.031
5. Gao, Q., Duan, C., Fan, H., & Meng, Q. (2008). Rotating machine fault diagnosis using empirical mode decomposition. Mechanical Systems and Signal Processing, 22(5), pp. 1072-1081. (In English). Available at: https://doi.org/10.1016/j.ymssp.2007.10.003
6. Heng, R. B. W, & Nor M. J. M. (1998). Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Applied Acoustic, 53(1-3), pp. 211-226. (In English). Available at: https://doi.org/10.1016/S0003-682X(97)00018-2
7. Lin, J., & Zuo, M. J. (2003). Gearbox fault diagnosis using adaptive wavelet filter. Mechanical Systems and Signal Processing, 17(6), pp. 1259-1269. (In English). Available at: https://doi.org/10.1006/mssp.2002.1507
8. Barkov, A. V., Barkova, N. A., & Azovtsev, A. Yu. (2000). Monitoring i diagnostika rotornykh mashin po vibratsii. Saint Petersburg, GMTU Publ., 158 p. (In Russian).
9. Chui, Ch. K. (1992). An Introduction to Wavelets. Texas, Academic Press. (In English). Available at: https://www.researchgate.net/publication/231222178_An_Introduction_to_Wavelets
10. Rusov, V. A. (1996). Spektral'naya vibrodiagnostika. Perm, 176 p. (In Russian).
11. Dobeshi, I. Desyat' lektsiy po veyvletam. (2001). Izhevsk: Regulyarnaya i khaoticheskaya dinamika NITS, 464 p. (In Russian).
12. Kostyukov, V. N. (1985). Rangovyy metod vibroakusticheskoy diagnostiki i otsenki kachestva mashin. Gidroprivod i sistemy upravleniya stroitel'nykh, tyagovykh i dorozhnykh mashin. Omsk: OmPI Publ., pp. 113-124. (In Russian).
13. Kostyukov, V. N. Sposob vibroakusticheskoy diagnostiki mashin periodicheskogo deystviya i ustroystvo dlya ego osushchestvleniya. Pat. № 1280961. RF MPK N04V 51/0060/M13/02. No 823505038/06. Applied. 22.10.82; Published: 20.08.98, Bull. 16. (In Russian).
14. Barszcz, T. (2009). Decomposition of vibration signals into deterministic and nondeter-ministic components and its capabilities of fault detection and identification. International Journal of Applied Mathematics and Computation Since, 19(2), pp. 327-335. (In English). DOI: 10.2478/v10006-009-0028-0
15. Harish Chandra, N., & Sekhar, A. S. (2016). Fault detection in rotor bearing systems using time frequency techniques. Mechanical Systems and Signal Processing, 72-73, pp. 105-133. (In English). Available at: https://doi.org/10.1016/j.ymssp.2015.11.013
16. Lv, Y., Yuan, R., & Song, G. (2016). Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing. Mechanical Systems and Signal Processing, 81, pp. 219-234. (In English). Available at: https://doi.org/10.1016/j.ymssp.2016.03.010
17. Antoni, J., & Randall, R. B. (2004). Unsupervised noise cancellation for vibration signals. Part I evaluation of adaptive algorithms // Mechanical Systems and Signal Processing, 18(1), pp. 89-101. (In English). Available at: https://doi.org/10.1016/S0888-3270(03)00012-8
18. Brie, D., Tomzak, M., Oehlmann, H., & Richard, A. (1997). Gear crack detection by adaptive amplitude and phase demodulation. Mechanical Systems and Signal Processing, 11(1), pp. 327-335. (In English). Available at: https://doi.org/10.1006/mssp.1996.0068
19. Chaturvedi, G. K., & Thomas, D. W. (1981). Adaptive noise cancelling and conditional monitoring. Journal of Sound Vibration, 76(3), pp. 391-405. (In English). Available at: https://doi.org/10.1016/0022-460X(81)90519-8
20. Dybala, J., & Zimroz, R. (2014). Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal. Applied Acoustic, 77, pp. 195-203. (In English). Available at: https://doi.org/10.1016/j.apacoust.2013.09.001
Review
For citations:
Kononov Р.S., Ogudova Е.V. Rationale for the use of vibration signal decomposition as a diagnostic method for rolling bearings of oil pumps. Oil and Gas Studies. 2019;(4):122-129. (In Russ.) https://doi.org/10.31660/0445-0108-2019-4-122-129
Views:
373
Email this article 
Post a Comment 