РОЗРОБКА КОНЦЕПЦІЇ ЦИФРОВОГО ДВІЙНИКА СИСТЕМИ ЕЛЕКТРОПРИВОДУ ЛІФТІВ НА ОСНОВІ СТОХАСТИЧНОЇ МОДЕЛІ ARIMAX

Стефан Зайченко; Євген Філатов

doi:10.20535/1813-5420.3.2026.359666

Vol. · Issue 3 · 2026 Oct 12, 2026 MONITORING, DIAGNOSTICS AND MANAGEMENT BY ENERGY PROCESSES AND EQUIPMENT

DEVELOPMENT OF A DIGITAL TWIN OF AN ELEVATOR ELECTRIC DRIVE SYSTEM BASED ON THE STOCHASTIC ARIMAX MODEL

Stefan Zaichenko Corresponding National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», Ukraine

Yevhen Filatov National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», Ukraine

Pages112-118 PublishedOct 12, 2026 LicenseOpen Access

PDF (Ukrainian)

VOL · ISS 3 · 2026 85 View issue

Abstract

Elevators are the most critical infrastructure elements of modern multi-storey buildings. The reliability of their electric drives directly affects passenger safety. The absence of tools for predicting degradation processes and for the analytical interpretation of diagnostic data leads to an increased number of emergency stops, higher specific energy losses, and accelerated wear of electromechanical systems. One of the promising approaches to solving these problems is the implementation of digital twins of electric drives. This approach enables the realization of predictive maintenance principles, whereby control and operational decisions are made based on the analysis of the state of a digital copy of the physical object that reproduces the dynamics of defects and deviations of parameters from their nominal values.

The aim of this study is to develop a prototype digital twin of an elevator electric drive capable of reproducing the dynamic operating modes of the electromechanical object in real time. This is achieved through the integration of physical, analytical, and sensor-based measurement data. Achieving this objective requires the comprehensive combination of measurement hardware, mathematical models of electromechanical processes, and specialized software for data acquisition, processing, analysis, and visualization.

Based on the research results, a structure for the digital twin of the elevator electric drive is proposed. It integrates mathematical models of electromagnetic and mechanical processes with real-time streams of experimental sensor data.

Keywords

#digital twin #mathematical model #reliability #elevator electric drive #induction motor #failure prediction

References

Zaichenko S., Kulish R., Derevianko D., Zhukova N. Justification of diagnostic parameters of autonomous electric power sources based on an internal combustion engine in the development of a technical diagnostics system // Power Engineering: Economics, Technique, Ecology. 2020. No. 3. P. 29–34.
Zaichenko S., Kulish R. Reduction of the degree of uncertainty in the technical condition of an autonomous power source // Materials of the V International Scientific and Practical Conference, April 5–7, 2021. Academy of Technical Sciences of Ukraine. Ivano-Frankivsk, 2021. Vol. 1. P. 175–178
3.Stefan Zaichenko, Ümran Erçetin, Roman Kulish, Denis Derevyanko, Vadim Shalenko. Determination of diagnostic parameters of power plants based on internal combustion engines. Mining Revue. Vol 3, 86-92p
4.Fuller A., Fan Z., Day C., Barclay P. Digital Twin: Enabling Technology, Challenges and Open Research. IEEE Access. 2020. Vol. 8. P. 108952–108971
5.Grieves M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. CIMdata, 2015. 28 p.
6. Tsvetkov O.V. Intelligent methods for failure prediction of power electrical equipment based on multilevel digital twins // Bulletin of the East Ukrainian National University named after Volodymyr Dahl. 2025. No. 9(295). P. 81–90. https://doi.org/10.33216/1998-7927-2025-295-9-81-90
7.Vladyslav Pliuhin, Vitalii Herasymenko, Oleh Kulaienko, Alina Trotsai, Gennadii Loktionov and Taras Sakhoshko. The State and Prospects of Technical Condition Diagnostics Methods for Electric Motors Considering the Digital Twin Concept. Lighting Engineering & Power Engineering 2025, Vol. 64, No. 2, 57–64 https://doi.org/10.33042/2079-424X.2025.64.2.02 11.
8. B. Lu, T.G. Habetler, R.G. Harley. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing. Vol. 149, 2021, Article 107262.
9. H. Henao, G.A. Capolino, M. Fernandez-Cabanas et al. Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE Industrial Electronics Magazine. Vol. 8, No. 2, 2014, pp. 31–42.
10. Y. Lei, N. Li, L. Guo et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing. Vol. 104, 2018, pp. 799–834.
11. A. Glowacz. Diagnostics of stator faults of the single-phase induction motor using thermal imaging and neural networks. Measurement. Vol. 93, 2016, pp. 86–93.
12. S. Li, B. Wang, Y. Dong. Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects. Energies. Vol. 14, No. 18, 2021, Article 5933.
13. M. Benbouzid, G.B. Kliman. A review of induction motor signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics. Vol. 47, No. 5, 2000, pp. 984–993.
W.T. Thomson, M. Fenger. Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine. Vol. 7, No. 4, 2001, pp. 26–34.
J. Lee, F. Wu, W. Zhao et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing. Vol. 42, No. 1–2, 2014, pp. 314–334.
M. Pecht, M. Kang. Prognostics and Health Management of Electronics. John Wiley & Sons, 2018. 302 p.
Z. Zhang, K. Zhou, C. Wang. Physics-of-failure-based prognostics and health management for power electronics. Microelectronics Reliability. Vol. 88–90, 2018, pp. 110–119.

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Previous in issue INVESTIGATION OF SYSTEM SAFETY IN LFP BACKUP POWER SYSTEMS AND SYNTHESIS OF A MULTI-LAYER PROTECTION ARCHITECTURE Next in issue DIGITAL TWIN STRUCTURE FOR SHORT-TERM ELECTRICAL LOAD FORECASTING AT A DAIRY PLANT

§ 06 — Related

[1] Zaichenko S., Kulish R., Derevianko D., Zhukova N. Justification of diagnostic parameters of autonomous electric power sources based on an internal combustion engine in the development of a technical diagnostics system // Power Engineering: Economics, Technique, Ecology. 2020. No. 3. P. 29–34.

[2] Zaichenko S., Kulish R. Reduction of the degree of uncertainty in the technical condition of an autonomous power source // Materials of the V International Scientific and Practical Conference, April 5–7, 2021. Academy of Technical Sciences of Ukraine. Ivano-Frankivsk, 2021. Vol. 1. P. 175–178

[3] 3.Stefan Zaichenko, Ümran Erçetin, Roman Kulish, Denis Derevyanko, Vadim Shalenko. Determination of diagnostic parameters of power plants based on internal combustion engines. Mining Revue. Vol 3, 86-92p

[4] 4.Fuller A., Fan Z., Day C., Barclay P. Digital Twin: Enabling Technology, Challenges and Open Research. IEEE Access. 2020. Vol. 8. P. 108952–108971

[5] 5.Grieves M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. CIMdata, 2015. 28 p.

[6] 6. Tsvetkov O.V. Intelligent methods for failure prediction of power electrical equipment based on multilevel digital twins // Bulletin of the East Ukrainian National University named after Volodymyr Dahl. 2025. No. 9(295). P. 81–90. https://doi.org/10.33216/1998-7927-2025-295-9-81-90

[7] 7.Vladyslav Pliuhin, Vitalii Herasymenko, Oleh Kulaienko, Alina Trotsai, Gennadii Loktionov and Taras Sakhoshko. The State and Prospects of Technical Condition Diagnostics Methods for Electric Motors Considering the Digital Twin Concept. Lighting Engineering & Power Engineering 2025, Vol. 64, No. 2, 57–64 https://doi.org/10.33042/2079-424X.2025.64.2.02 11.

[8] 8. B. Lu, T.G. Habetler, R.G. Harley. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing. Vol. 149, 2021, Article 107262.

[9] 9. H. Henao, G.A. Capolino, M. Fernandez-Cabanas et al. Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE Industrial Electronics Magazine. Vol. 8, No. 2, 2014, pp. 31–42.

[10] 10. Y. Lei, N. Li, L. Guo et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing. Vol. 104, 2018, pp. 799–834.

[11] 11. A. Glowacz. Diagnostics of stator faults of the single-phase induction motor using thermal imaging and neural networks. Measurement. Vol. 93, 2016, pp. 86–93.

[12] 12. S. Li, B. Wang, Y. Dong. Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects. Energies. Vol. 14, No. 18, 2021, Article 5933.

[13] 13. M. Benbouzid, G.B. Kliman. A review of induction motor signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics. Vol. 47, No. 5, 2000, pp. 984–993.

[14] W.T. Thomson, M. Fenger. Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine. Vol. 7, No. 4, 2001, pp. 26–34.

[15] J. Lee, F. Wu, W. Zhao et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing. Vol. 42, No. 1–2, 2014, pp. 314–334.

[16] M. Pecht, M. Kang. Prognostics and Health Management of Electronics. John Wiley & Sons, 2018. 302 p.

[17] Z. Zhang, K. Zhou, C. Wang. Physics-of-failure-based prognostics and health management for power electronics. Microelectronics Reliability. Vol. 88–90, 2018, pp. 110–119.

DEVELOPMENT OF A DIGITAL TWIN OF AN ELEVATOR ELECTRIC DRIVE SYSTEM BASED ON THE STOCHASTIC ARIMAX MODEL

Abstract

Keywords

References

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