Ensuring safety and energy efficiency of electrical mining complexes based on the diagnostic curve in load nodes

AC electric motors are a key and widely used component of process equipment at mining facilities, including mines, quarries, and processing plants. Their operation is critical for mechanisms such as conveyors, crushers, mills, pumps, and fans. Timely assessment of the technical condition of an electric motor is a special challenge, as its failure can lead not only to the shutdown of an individual unit but also to the downtime of the entire process chain, resulting in significant economic losses and safety risks. Currently, a number of methods exist for diagnosing electric motors and identifying defects. However, many of these require specialized diagnostic equipment, the cost of which often significantly exceeds the purchase price of the motor itself, making it uneconomical for widespread use on numerous pieces of equipment. This paper proposes a simplified approach to assessing the condition of an electric motor, adapted for mining applications. The method involves constructing and analyzing a diagnostic curve based on data from current sensors, which are typically already installed on the electric motor as part of the control and protection systems. This curve, obtained using a modification of the vector (Park) transform method, represents a simple and accessible method for obtaining information on the motor’s condition in real time without the need for expensive additional equipment. This article describes the algorithm for generating the diagnostic curve and demonstrates its effectiveness using model and full-scale experiments. The model experiment simulated rotor and stator faults typical of severe operating conditions. Full-scale experiments were conducted on operating equipment to evaluate the response of the diagnostic curve to changes in mechanical load, similar to real-world operating conditions of mining complexes.

Korolev N. A. Ensuring safety and energy efficiency of electrical mining complexes based on the diagnostic curve in load nodes. MIAB. Mining Inf. Anal. Bull. 2025;(11-1):166—182. [In Russ]. DOI: 10.25018/0236_1493_2025_111_0_166.

Korolev N. A., Cand. Sci, Principal Scientist, https://orcid.org/0000-0002-0583-9695,  Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia, e-mail: korolev_na@pers.spmi.ru.

1. Dolzhikov V. V., Ryadinsky D. E., Yakovlev A. A. Influence of deceleration intervals on the amplitudes of stress waves during the explosion of a system of borehole charges. MIAB. Mining Inf. Anal. Bull. 2022, no. 6−2, pp. 18–32. DOI: 10.25018/0236_1493_2022_62_0_18.
2. Khokhlov S., Abiev Z., Makkoev V. The choice of optical flame detectors for automatic explosion containment systems based on the results of explosion radiation analysis of methane-and dust-air mixtures. Applied Sciences. 2022, vol. 12, no. 3, p. 1515. https://doi.org/10.3390/app12031515.
3. Romashev A. O., Nikolaeva N. V., Gatiatullin B. L. Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition. Journal of Mining Institute. 2022, vol. 256, pp. 677–685. [In Russ]. DOI: 10.31897/PMI.2022.77.
4. Yemelyanov V., Chernyi S., Yemelyanova N., Varadarajan V. Application of neural networks to forecast changes in the technical condition of critical production facilities. Computers & Electrical Engineering. 2021, vol. 93, p. 107225. https://doi.org/10.1016/j.compeleceng.2021.107225.
5. Gizatullin R., Dvoynikov M., Romanova N., Nikitin V. Drilling in gas hydrates: Managing gas appearance risks. Energies. 2023, vol. 16, no. 5, p. 2387. https://doi.org/10.3390/en16052387.
6. Smirnov N. I., Drozdov A. N., Smirnov N. N. Tribodynamic aspects of the resource of electric submersible vane pumps for oil production. Journal of Mining Institute. 2023, vol. 264, p. 962–970. [In Russ].
7. Litvinenko V. S. Digital economy as a factor in the technological development of the mineral sector. Natural Resources Research. 2020, vol. 29, no. 3, pp. 1521–1541. https://doi.org/10.1007/s11053-019-09568-4.
8. Chen Y., Wang Y., Zhao C. From riches to digitalization: The role of AMC in overcoming challenges of digital transformation in resource-rich regions. Technological Forecasting and Social Change. 2024, vol. 200, p. 123153. https://doi.org/10.1016/j.techfore.2023.123153.
9. Cherepovitsyn A. E., Tretyakov N. A. Development of a new assessment system for the applicability of digital projects in the oil and gas sector. Journal of Mining Institute. 2023, vol. 262, p. 628–642. [In Russ].
10. Fomin S. I., Govorov A. S. Validation of the chosen cutoff grade value in open pit mine design. Mining Informational and Analytical Bulletin. 2023, no. 12, pp. 169–182. DOI: 10.25018/0236_ 1493_2023_12_0_169.
11. Simakov A. S., Trifonova M. E., Gorlenkov D. V. Virtual analyzer of the voltage and current spectrum of the electric arc in electric arc furnaces. Russian Metallurgy (Metally). 2021, vol. 2021, no. 6, pp. 713–719. https://doi.org/10.1134/S0036029521060252.
12. Khokhlov S., Safina E., Vasiliev V. Risk-oriented approach implementation in departments ranking and teaching staff motivation. International Journal for Quality Research. 2018, vol. 12, no. 2, p. 501. DOI: 10.18421/IJQR12.02−12.
13. Sanjin Braut, Eerik Sikanen, Janne Nerg, Jussi Sopanen, Željko Božić. Fatigue life prediction of Electric RaceAbout (ERA) traction motor rotor. Procedia structural integrity. 2021, vol. 31, pp. 45–50. https://doi.org/10.1016/j.prostr.2021.03.024.
14. Mian Z., Deng X., Dong X., Tian Y., Cao T., Chen K., Al Jaber T. A literature review of fault diagnosis based on ensemble learning. Engineering Applications of Artificial Intelligence. 2024, vol. 127, p. 107357. https://doi.org/10.1016/j.engappai.2023.107357.
15. Nevskaya M., Shabalova A., Kosovtseva T., Nikolaychuk L. Applications of simulation modeling in mining project risk management: criteria, algorithm, evaluation. Journal of Infrastructure, Policy and Development. 2024, vol. 8(8), p. 5375. DOI: 10.24294/jipd.v8i8.5375.
16. D’Urso D., Chiacchio F., Borrometi D., Costa A., Compagno L. Dynamic failure rate model of an electric motor comparing the Military Standard and Svenska Kullagerfabriken (SKF) methods. Procedia Computer Science. 2021, vol. 180, pp. 456–465. https://doi.org/10.1016/j.procs.2021.01.262.
17. Aizpurua J. I., Knutsen K. E., Heimdal M., Vanem E. Integrated machine learning and probabilistic degradation approach for vessel electric motor prognostics. Ocean Engineering. 2023, vol. 275, p. 114153. https://doi.org/10.1016/j.oceaneng.2023.114153.
18. Yuan Gao, Cheong B., Bozhko S., Wheeler P., Gerada C., Tao Yang. Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications. Chinese Journal of Aeronautics. 2023, vol. 36, no. 2, pp. 213–228. https://doi.org/10.1016/j.cja.2022.08.011.
19. Zhukovskiy Yu., Tsvetkov P., Koshenkova A., Ivan Skvortsov, Iuliia Andreeva, Valeriya Vorobeva. A Methodology for Forecasting the KPIs of a Region’s Development: Case of the Russian Arctic. Sustainability. 2024, vol. 16, no. 15, p. 6597. DOI: 10.3390/su16156597.
20. Abramik S., Sleszynski W., Nieznanski J., Piquet H. A diagnostic method for on-line fault detection and localization in VSI-fed AC drives. 10th European Conference on Power Electronics and Applications, EPE’2003. 2003.
21. Junior R. F. R. et al. Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals. Measurement. 2022, vol. 190, p. 110759. https://doi.org/10.1016/j.measurement.2022.110759.
22. Duda A., Drozdowski P. Induction motor fault diagnosis based on zero-sequence current analysis. Energies. 2020, vol. 13, no. 24, p. 6528. https://doi.org/10.3390/en13246528.
23. Wang Z., Shi D., Xu Y., Zhen D., Gu F., Ball A. D. Early rolling bearing fault diagnosis in induction motors based on on-rotor sensing vibrations. Measurement. 2023, vol. 222, p. 113614. https://doi.org/10.1016/j.measurement.2023.113614.
24. Wu F., Zhao J. A real-time multiple open-circuit fault diagnosis method in voltage-source-inverter fed vector-controlled drives. IEEE Transactions on Power Electronics. 2015, vol. 31, no. 2, pp. 1425–1437. DOI: 10.1109/TPEL.2015.2422131.
25. Nevskaya M., Shabalova A., Nikolaichuk L., & Kirsanova N. Development of a Quantitative Assessment Algorithm for Operational Risks in Mining Engineering. Resources. 2025, vol. 14(4), p. 53. https://doi.org/10.3390/resources14040053.
26. Cornell E. P., Lipo T. A. Modeling and design of controlled current induction motor drive systems. IEEE Transactions on Industry Applications. 1977, no. 4, pp. 321–330. DOI: 10.1109 / TIA.1977.4503414.
27. Thomson W. T., Fenger M. Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine. 2001, vol. 7, no. 4, pp. 26–34. DOI: 10.1109 / 2943.930988. 
28. Fenger M., LLoyd B. A., Thomson W. T. Development of a tool to detect faults in induction motors via current signature analysis. Cement Industry Technical Conference, 2003. Conference Record. IEEE-IAS/PCA 2003. IEEE, 2003, pp. 37–46. DOI: DOI:10.1109/CITCON.2003.1204707.
29. Certificate of state registration of computer program No. 2024616314 Russian Federation. Program for identifying diagnostic characteristics of AC electric drives in real time: No. 2024614818: declared 12.03.2024: published 19.03.2024 / N. A. Korolev, N. I. Koteleva; applicant Federal State Budgetary Educational Institution of Higher Education “Empress Catherine II Saint Petersburg Mining University”. 
30. Koteleva N. I., Korolev N. A., Zhukovskiy Y. L. Identification of the technical condition of induction motor groups by the total energy flow. Energies. 2021, vol. 14, no. 20. [In Russ]. DOI: 10.3390/en14206677.