DOI: https://doi.org/10.15587/1729-4061.2019.155839

The development of methods for determining vibration stochastic fields of technological complexes

Nadiia Marchenko, Olena Monchenko, Ganna Martyniuk

Abstract


The force effects occurring in technological complexes have been studied on the basis of the analysis of technical diagnostics system. Due to the distinction between deterministic and random force effects, there have been proposed various methods to distinguish the vibration of informational diagnostic characteristics in order to ensure prompt and reliable detection of the rapidly developing defects. Reliable diagnostics will make it possible to switch from a system of scheduled preventive repairs to the organization of repairs for the current state, with a decrease in the cost of repairing and rebuilding the units of technological complexes by early detection of the defects emerging in the assembly components.

On the basis of analyzing the process of propagation of vibroacoustic waves caused by the power action, there has been developed a mathematical model for the emergence and propagation of elastic waves in sophisticated technological complexes from the places of their origin to the point of observation. There have been suggested kinematic schemes for propagation of low-frequency vibrations, vibrosignals from the brush-collector unit, as well as waves from the inner ring of the bearing. This makes possible to substantiate a mathematical model of the occurrence and propagation of vibroacoustic waves in the parts and units of technological complexes from various sources of vibration.

The comparative analysis of the research findings on the real vibration fields and the results of numerical modeling confirms the adequacy of the model to the real process. The article presents the graphs of the temporal realization of signals in the model, the spectra of the realized signals, as well as their autocorrelation functions reflecting the main characteristics of the signals at the measurement point. The findings can be used to diagnose and reduce the cost of repair and restoration of the units in sophisticated technological complexes by early detection of the defects emerging in the assembly parts.


Keywords


vibration signal; stochastic; rolling bearings; vibration fields; vibroacoustic waves; shock pulse

References


Marchenko, N. B., Nechyporuk, V. V., Nechyporuk, O. P., Pepa, Yu. V. (2014). Metody otsiniuvannia tochnosti informatsiyno-vymiriuvalnykh system diahnostyky. Kyiv: Vyd-vo PVP «Zadruha», 200.

Marchenko, N. B., Nechiporuk, E. P. (2012). Prichiny vozniknoveniya i klassifikaciya otkazov v tekhnicheskih sistemah. Suchasnyi zakhyst informatsiyi, 4, 84–87.

Marchenko, N. B., Nechyporuk, O. P., Vakhil, A. A., Shukalo, V. V. (2014). Metody obrobky vibrodiahnostychnoi informatsiyi ta pobudova na yikh osnovi system operatyvnoi diahnostyky elektrotekhnichnoho obladnannia. The Caucasus Economical and social analysis journal of southern Caucasus, 3, 25–29.

Ge, M., Wang, J., Ren, X. (2017). Fault Diagnosis of Rolling Bearings Based on EWT and KDEC. Entropy, 19 (12), 633. doi: https://doi.org/10.3390/e19120633

Kumar, M. S., Prabhu, B. S. (2000). Rotating Machinery Predictive Maintenance Through Expert System. International Journal of Rotating Machinery, 6 (5), 363–373. doi: https://doi.org/10.1155/s1023621x00000348

Xu, X., Han, Q., Chu, F. (2018). Review of Electromagnetic Vibration in Electrical Machines. Energies, 11 (7), 1779. doi: https://doi.org/10.3390/en11071779

Wang, F., Liu, X., Liu, C., Li, H., Han, Q. (2018). Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1,1) Model. Shock and Vibration, 2018, 1–10. doi: https://doi.org/10.1155/2018/3013684

Abuthakeer, S. S., Mohanram, P. V., Mohankumar, G. (2011). The Effect of Spindle Vibration on Surface Roughness of Workspiece in Dry Turning Using Ann. International Journal of Lean Thinking, 2 (2), 42–58.

Li, Y., Wang, L., Guan, J. (2017). A Spectrum Detection Approach for Bearing Fault Signal Based on Spectral Kurtosis. Shock and Vibration, 2017, 1–9. doi: https://doi.org/10.1155/2017/6106103

Lv, Y., Zhang, Y., Yi, C. (2018). Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis. Entropy, 20 (12), 920. doi: https://doi.org/10.3390/e20120920

Zhitomerskiy, V. K. (1966). Mekhanicheskie kolebaniya i praktika ih ustraneniya. Moscow: Mashinostroenie, 175.

Tihonov, V. I. (1982). Statisticheskaya radiotekhnika. Moscow: Radio i svyaz', 624.

Korn, G., Korn, T. (1977). Spravochnik po matematike dlya nauchnyh rabotnikov i inzhenerov. Moscow: Nauka, 456.

Gioev, Z. T., Golov, Yu. V. et. al. (2007). Sposoby vydeleniya iz sobstvennoy korpusnoy vibracii dvigatelya skrytoy periodicheskoy sostavlyayushchey – defekta. Elektrovozostroenie, 38, 308–319.

Vol'dek, A. I. (2008). Elektricheskie mashiny. Vvedenie v elektromekhaniku. Mashiny postoyannogo toka i transformatory. Sankt-Peterburg: Piter, 320.


GOST Style Citations


Metody otsiniuvannia tochnosti informatsiyno-vymiriuvalnykh system diahnostyky: monohrafiya / Marchenko N. B., Nechyporuk V. V., Nechyporuk O. P., Pepa Yu. V. Kyiv: Vyd-vo PVP «Zadruha», 2014. 200 p.

Marchenko N. B., Nechiporuk E. P. Prichiny vozniknoveniya i klassifikaciya otkazov v tekhnicheskih sistemah // Suchasnyi zakhyst informatsiyi. 2012. Issue 4. P. 84–87.

Metody obrobky vibrodiahnostychnoi informatsiyi ta pobudova na yikh osnovi system operatyvnoi diahnostyky elektrotekhnichnoho obladnannia / Marchenko N. B., Nechyporuk O. P., Vakhil A. A., Shukalo V. V. // The Caucasus Economical and social analysis journal of southern Caucasus. 2014. Issue 3. P. 25–29.

Ge M., Wang J., Ren X. Fault Diagnosis of Rolling Bearings Based on EWT and KDEC // Entropy. 2017. Vol. 19, Issue 12. P. 633. doi: https://doi.org/10.3390/e19120633 

Kumar M. S., Prabhu B. S. Rotating Machinery Predictive Maintenance Through Expert System // International Journal of Rotating Machinery. 2000. Vol. 6, Issue 5. P. 363–373. doi: https://doi.org/10.1155/s1023621x00000348 

Xu X., Han Q., Chu F. Review of Electromagnetic Vibration in Electrical Machines // Energies. 2018. Vol. 11, Issue 7. P. 1779. doi: https://doi.org/10.3390/en11071779 

Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model / Wang F., Liu X., Liu C., Li H., Han Q. // Shock and Vibration. 2018. Vol. 2018. P. 1–10. doi: https://doi.org/10.1155/2018/3013684 

Abuthakeer S. S., Mohanram P. V., Mohankumar G. The Effect of Spindle Vibration on Surface Roughness of Workspiece in Dry Turning Using Ann // International Journal of Lean Thinking. 2011. Vol. 2, Issue 2. P. 42–58.

Li Y., Wang L., Guan J. A Spectrum Detection Approach for Bearing Fault Signal Based on Spectral Kurtosis // Shock and Vibration. 2017. Vol. 2017. P. 1–9. doi: https://doi.org/10.1155/2017/6106103 

Lv Y., Zhang Y., Yi C. Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis // Entropy. 2018. Vol. 20, Issue 12. P. 920. doi: https://doi.org/10.3390/e20120920 

Zhitomerskiy V. K. Mekhanicheskie kolebaniya i praktika ih ustraneniya. Moscow: Mashinostroenie, 1966. 175 p.

Tihonov V. I. Statisticheskaya radiotekhnika. 2-e izd., pererab. i dop. Moscow: Radio i svyaz', 1982. 624 p.

Korn G., Korn T. Spravochnik po matematike dlya nauchnyh rabotnikov i inzhenerov. Moscow: Nauka, 1977. 456 p.

Sposoby vydeleniya iz sobstvennoy korpusnoy vibracii dvigatelya skrytoy periodicheskoy sostavlyayushchey – defekta / Gioev Z. T., Golov Yu. V. et. al. // Elektrovozostroenie. 2007. Vol. 38. P. 308–319.

Vol'dek A. I. Elektricheskie mashiny. Vvedenie v elektromekhaniku. Mashiny postoyannogo toka i transformatory. Sankt-Peterburg: Piter, 2008. 320 p.







Copyright (c) 2019 Nadiia Marchenko, Olena Monchenko, Ganna Martyniuk

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN (print) 1729-3774, ISSN (on-line) 1729-4061