Vol. 337 No. 8 (2026)

DOI https://doi.org/10.18799/24131830/2026/8/5495

Average flow rate effect on the reliability of submersible electric motors of borehole pumping units in uranium mining by the in-situ leaching method

Relevance. The reliability of borehole pumping units used for uranium in-situ leaching mining is largely determined by the performance of submersible electric motors. Operating a submersible electric motor in modes other than its nominal value reduces the expected service life of the equipment. Refining the probabilistic reliability model of electric motor, taking into account its load determined by the average well flow rate, leads to increased accuracy in calculating the need for new electric motors and directly impacts production profitability. Methods. Statistical methods, survival analysis, statistical hypothesis testing. Results and conclusions. For a previously obtained probabilistic reliability model of a submersible electric motor based on a mixture of two Weibull distributions, the parameter values were determined for groups of electric motors operating under different loads. The dependences of five reliability model parameters on the average flow rate were determined as piecewise linear approximations; their adequacy was confirmed using nonparametric goodness-of-fit tests. Based on the refined probabilistic reliability model, the dependence of the expected mean time to failure of a submersible electric motor on the average well flow rate was obtained. It was shown that operating an electric motor with flow rates significantly different from the nominal ones leads to a noticeable decrease in the mean time to failure. The resulting refined probabilistic reliability model can be used in information and control systems of a production complex to forecast the service life of submersible electric motors, as well as to calculate the need for electric motors required to replace failed ones over the planning horizon.

For citation: Efremov A.A., Noskov M.D., Filipas A.A., Shchipkov А.А. Average flow rate effect on the reliability of submersible electric motors of borehole pumping units in uranium mining by the in-situ leaching method. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2026, vol. 337, no. 8, pp. 200–210. http://doi.org/10.18799/24131830/2026/8/5495

Keywords:

probabilistic reliability model, submersible electrical motor, uranium mining, in-situ leaching, average flow rate, nonparametric goodness-of-fit tests

Authors:

Alexander A. Efremov

Mikhail D. Noskov

Alexander A. Filipas

Alexander A. Shchipkov

References:

1. Svyatetsky V.S., Solodov I.N. Technological advancement strategy of uranium mining industry in Russia. Mining Journal, 2015, no. 7, pp. 68–77. (In Russ.)

2. Sreenivas T., Kalburgi A.K., Sahu M.L., Roy S.B. Exploration, mining, milling and processing of uranium. Nuclear Fuel Cycle. Eds. Tomar B.S., Rao P.R.V., Roy S.B., Panakkal J.P., Raj K., Nandakumar A.N. Singapore, Springer, 2023. pp. 17–79. DOI: 10.1007/978-981-99-0949-0_2.

3. Geotechnology of uranium (Russian experience): monograph. Ed. by I.N. Solodov, E.N. Kamnev. Moscow, KDU, Universitetskaya Kniga Publ., 2017. 576 p. (In Russ.)

4. Zeng S., Sun J., Sun B. Experimental investigation on the influence of surfactant to the seepage characteristics of acid leaching solution during in-situ leaching of uranium. J Radioanal Nucl Chem, 2023, vol. 332, pp. 3651–3660. DOI: 10.1007/s10967-023-09038-5.

5. Istomin A.D., Babkin A.S., Noskov M.D., Cheglokov A.A., Poponin N.A. Technological information system for monitoring and managing the mining complex of an enterprise for the extraction of uranium using the in-situ leaching method. Non-Ferrous Metals, 2012, no. 1/3, pp. 16–22. (In Russ.)

6. Valitov S.N., Istomin A.D., Noskov M.D., Cheglokov A.A. Program module for accounting, control and analysis of pumping equipment at uranium mining enterprise. Russian Physics Journal, 2017, vol. 60, no. 9/2, pp. 18–23. (In Russ.)

7. Efremov A.A., Kadyrov K.K., Noskov M.D., Filipas A.A., Shchipkov A.A. Probabilistic reliability model of electrical motors in submersible well pumping units used in the uranium mining by in-situ leaching. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2024, vol. 335, no. 10, pp. 253–264. (In Russ.) DOI: 10.18799/24131830/2024/10/4721.

8. Gutsul M.V., Noskov M.D., Sakirko G.K. Software for predictive analytics of the operation of submersible pumping units of the geotechnical enterprise JSC Dalur. Certificate of State Registration of a Computer Program, no. 2024662512, Russian Federation, 2024. (In Russ.)

9. SS R 50779.27-2017 Statistical methods. Weibull distribution. Data analysis. Moscow, Standartinform Publ., 2020. 62 p. (In Russ.) Available at: https://protect.gost.ru/document.aspx?control=7&id=218209 (accessed 23 January 2024).

10. Kumar S., Jain M. Shifted Mixture Model Using Weibull, Lognormal, and Gamma Distributions. National Academy Science Letters, 2023, no. 46, pp. 539–545. DOI: 10.1007/s40009-023-01287-y.

11. Wu Y., Lu Z., Wu J. Reliability evaluation of components with multiple failure modes based on mixture Weibull distribution using expectation maximization algorithm. Journal of Mechanical Science and Technology, 2024, vol. 38, pp. 649–660. DOI: 10.1007/s12206-024-0113-1

12. Lee H. Methods for censored survival time data. Foundations of Applied Statistical Methods. Cham, Springer, 2023. pp. 141–145. DOI: 10.1007/978-3-031-42296-6_7

13. James G., Witten D., Hastie T., Tibshirani R., Taylor J. Survival analysis and censored data. An Introduction to Statistical Learning. Springer Texts in Statistics. Cham, Springer, 2023. pp. 469–502. DOI: 10.1007/978-3-031-38747-0_11.

14. Kvam P., Lu J.C. Statistics for reliability modeling. Springer Handbook of Engineering Statistics.Ed. by H. Pham. London, Springer, 2023. pp. 53–65. DOI: 10.1007/978-1-4471-7503-2_3.

15. James G., Witten D., Hastie T., Tibshirani R. Introduction to statistical learning with applications in Python. Moscow, DMK Press, 2024. 846 p. (In Russ.) Available at: https://e.lanbook.com/book/464258 (accessed 26 December 2025).

16. Janssen P., Veraverbeke N. Nonparametric estimation of univariate and bivariate survival functions under right censoring: a survey. Metrika, 2024, vol. 87, pp. 211–245. DOI: 10.1007/s00184-023-00911-7

17. Lemeshko B.Yu. On problems and errors in applying goodness-of-fit tests. Bulletin of Tomsk State University. Control, Computing and Information Science, 2023, no. 64, pp. 74–90. (In Russ.)

18. Chimitova E., Nikulin M., Lemeshko B. Nonparametric goodness-of-fit tests for censored data. Proceedings of the 7th International Conference on Mathematical Methods in Reliability Theory, Methods and Applications. Beijing, 2011. pp. 817–823.

19. Lemeshko B.Yu. Nonparametric goodness-of-fit tests: application guide. Moscow, Scientific Publishing Center INFRA-M Ltd, 2014. 163 p. (In Russ.)

20. Lemeshko B.Yu., Gorbunova A.A., Lemesko S.B., Rogozhnikov A.P. On solving the problems of applying some nonparametric goodness-of-fit tests. Autometry, 2014, vol. 50, no. 1, pp. 26–43. (In Russ.)