Modeling degradation of the actual state of rolling bearings based on a unified diagnostic criterion
P.B. Gerike1, B.L. Gerike1, 2
1 Federal Research Center for Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation
2 T.F. Gorbachev Kuzbass State Technical University, Kemerovo, Russian Federation
Russian Mining Industry №S2 / 2023 р. 32-36
Abstract:
Background. Without any claims for comprehensive coverage, this article presents some results of modeling technical degradation of the rolling bearings used in the power and mechanical components of mining machines. The results of comprehensive diagnostics of mining equipment based on vibration parameters made it possible to propose a new approach to modeling the processes of changing the actual state of mining machines.
Objective. The research aims at performing mid-term forecasting of defect development in the rolling bearings installed in the power and mechanical components of mining machines as well as at demonstrating the efficiency of the new unified diagnostic criterion when it is used as a simulated parameter in an adaptive mathematical model of degradation.
Research methods. Results of a complex diagnostic approach to analyzing vibration parameters were applied in the this research, including the spectral analysis in the extended frequency and dynamic range, spectrum envelope and kurtosis analysis. The results obtained confirm the efficiency of the proposed set of diagnostic features and defect detection rules in analyzing mechanical vibration parameters as applied to the task of creating a unified diagnostic criterion for assessing the technical condition of rolling bearings.
Results. The obtained research results prove the fundamental efficiency of the proposed solution to the challenges concerned with comprehensive approach to analyzing the vibration parameters of mining equipment and modeling the processes of changes in the actual technical condition of the power and mechanical components of mining machines using a new unified criterion for diagnostics of rolling bearings. The research results can be applied in designing the core elements in the maintenance strategy for mining machines according to their actual condition, which will minimize unproductive downtime of the mining fleet and increase the overall level of safety in surface mining operations by reducing the number of equipment which technical condition is unacceptable.
Keywords: vibration analysis, rolling bearings, mining shovels, single diagnostic criterion, maintenance management, mechanical defects, predictive modeling
Acknowledgments: The work was performed within the framework of the state assignment of the Federal Research Center of Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences, Project FWEZ-2021-0002 'Development of efficient technologies of coal mining by robotic mining complexes operating without permanent presence of personnel in mining zones, design of control systems and methods to assess their technical condition and operating life as well as justification of the mineral resource base reproduction' (Reg. No. АААА-А21-121012290021-1).
For citation: Gerike P.B., Gerike B.L. Modeling degradation of the actual state of rolling bearings based on a unified diagnostic criterion. Russian Mining Industry. 2023;(S2):32–36. https://doi.org/10.30686/1609-9192-2023-S1-32-36
Article info
Received: 19.07.2023
Revised: 14.08.2023
Accepted: 14.08.2023
Information about the authors
Pavel B. Gerike – Cand. Sci. (Eng.), Associate Professor, Senior Research Associate, Coal Engineering Laboratory, Federal Research Center for Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences, Kemerovo, Russian Federation; https://orcid.org/0000-0003-2085-6108; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Boris L. Gerike – Dr. Sci. (Eng.), Professor, Chief Research Associate, Federal Research Center for Coal and Coal Chemistry of the Siberian Branch of the Russian Academy of Sciences; Professor, Department of Mining Machines and Complexes, T.F. Gorbachev Kuzbass State Technical University, Kemerovo, Russian Federation; https://orcid.org/0000-0001-9586-8723; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Conflict of interest
The authors declare no conflict of interests. All the authors have read and approved the final version of this paper.
References
1. Klyuev V.V. (ed.). Non-destructive testing. Handbook. Moscow: Mashinostroenie; 2005. Vol. 7. 828 p. (In Russ.)
2. Sushko A.E. Development special mathematical and software for automated diagnosis of complex systems. [PhD thesis excerpt]. Moscow; 2007. 170 p. (In Russ.)
3. Puchalski A., Komorska I. Stable distributions and fractal diagnostic models of vibration signals of rotating systems. In: Timofiejczuk A., Chaari F., Zimroz R., Bartelmus W., Haddar M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2016. Springer; 2018. Vol. 9, pp. 91–101. https://doi.org/10.1007/978-3-319-61927-9_9
4. Klishin V.I., Gerike P.B., Gerike B.L. Tools and end effectors for mining of hard minerals. Mining Informational and Analytical Bulletin. 2015;(S60-1):143–150. (In Russ.) Available at: https://www.elibrary.ru/hdjfzf
5. Balducchi F., Arghir M., Gaudillere S. Experimental analysis of the unbalance response of rigid rotors supported on aerodynamic foil bearings. In: Proceedings of the ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. Volume 7B: Structures and Dynamics, Düsseldorf, Germany, June 16–20, 2014. ASME; 2014. V07BT32A009. https://doi.org/10.1115/GT2014-25552
6. Puchalski A. A technique for the vibration signal analysis in vehicle diagnostics. Mechanical Systems and Signal Processing. 2015;5657:173–180. https://doi.org/10.1016/j.ymssp.2014.11.007
7. Trebuna F., Šimčák F., Bocko J., Hunady R., Pastor M. Complex approach to the vibrodiagnostic analysis of excessive vibration of the exhaust fan. Engineering Failure Analysis. 2014;37:86–95. https://doi.org/10.1016/j.engfailanal.2013.11.015
8. Gerike P.B., Gerike B.L., Shakhmanov V.N. Dynamic diagnostics of mechanical assemblies of mining equipment. Mining Informational and Analytical Bulletin. 2011;(S5):80–89. (In Russ.) Available at: https://www.elibrary.ru/onbvwn
9. Gertsbakh I. Reliability theory with applications to preventive maintenance. Springer; 2000. 219 p. https://doi.org/10.1007/978-3-662-04236-6
10. Shardakov I., Shestakov A., Tsvetkov R., Yepin V. Crack diagnostics in a large-scale reinforced concrete structure based on the analysis of vibration processes. AIP Conference Proceedings. 2018;2053:040090. https://doi.org/10.1063/1.5084528
11. Shirman A.R., Soloviev A.B. The practical vibration analysis and monitoring of mechanical equipment. Moscow: Nauka; 1996. 276 p. (In Russ.)
12. Barkov A.V., Barkova N.A. Vibration diagnostics of machines and equipment. Vibration analysis. St. Petersburg: SMTU; 2004. 156 p. (In Russ.)
13. Wang T., Han Q., Chu F., Feng Z. Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review. Mechanical Systems and Signal Processing. 2019;126:662–685. https://doi.org/10.1016/j.ymssp.2019.02.051
14. Ghasemloonia A., Rideout D.G., Butt S.D. Vibration analysis of a drillstring in vibration-assisted rotary drilling: finite element modeling with analytical validation. Journal of Energy Resources Technology. 2013;135(3):032902. https://doi.org/10.1115/1.4023333
15. Gerike B.L., Gerike P.B. Discussion of industrial validation results and directions of further research into layer-wise mining cutters. Bulletin of the Kuzbass State Technical University. 2006;(1):48–51. (In Russ.) Available at: https://www.elibrary.ru/kxgqmh
