Intelligent system for energy monitoring and predictive diagnostics of a screw compressor unit based on a hybrid digital twin
DOI:
https://doi.org/10.31548/energiya4(86).2026.127Keywords:
hybrid digital twin, screw compressor, energy efficiency, LSTM, Grey-Box modelling, predictive diagnostics, energy monitoring, condition monitoringAbstract
The paper presents a comprehensive architecture of an intelligent system for energy monitoring and predictive diagnostics of a multi-stage compressor complex, based on a hybrid digital twin. The proposed approach combines a physics-based model of electromechanical energy conversion (Park's equations) with an adaptive corrector based on a recurrent LSTM neural network, implementing a Grey-Box concept to improve modelling and diagnostic accuracy. The architecture is complementary to low-level induction motor state observers embedded in commercial frequency converters, addressing a higher-hierarchy task – forming an aggregated energy signature of the unit over an hours-to-days horizon. Experiments were performed on two industrial screw compressor units: a variable-speed drive (200 kW, VFD) and a fixed-speed drive (250 kW). Validation on real production data (7-day horizon, 60-second SCADA sampling) confirmed the hybridization effect: for the VFD-driven unit, the mean absolute error decreased from 34.00 A (physical model) to 13.17 A (hybrid model), a 61.3% improvement; for the fixed-speed unit, from 100.29 A to 26.72 A, a 73.4% improvement. The resulting "Healthy Baseline" provides increased sensitivity to slow integral degradation processes – progressive internal leakage of the screw pair, thermal drift of winding resistances, and efficiency degradation – at deviations below 5% from nominal operation. The results demonstrate the architecture's potential to improve energy efficiency and reduce maintenance costs within the Industry 4.0 framework. Ref. 35, Fig. 3, Tab. 1.
Recieved: 01.07.2026. Recieved: 16.08.2026. Accepted: 20.08.2026.
References
1. Sharapov, S. O. (2020). Energy conservation in compressor systems (Textbook). Sumy State University. [in Ukrainian]
2. Pronin, V. A., et al. (2021). Methods for regulating the performance of screw compressors and features applications for single rotor machines. AIP Conference Proceedings, 2412(1). https://doi.org/10.1063/5.0075107
3. Morais, D., et al. (2022). Energy consumption and efficiency measures in the Portuguese food processing industry. Journal of Food Processing and Preservation, 46(8), e14862. https://doi.org/10.1111/jfpp.14862
4. Nitsak, Y. M., & Bosak, A. V. (2026). Predicting the energy efficiency of screw compressors of ammonia refrigeration units with frequency control based on a neural network. System Research in Energy, 1(85), 94–106. [in Ukrainian] https://doi.org/10.15407/srenergy2026.01.094
5. Hedayati Kia, S., et al. (2025). Real-time digital twins for intelligent fault diagnosis and condition-based monitoring of electrical machines. Energies, 18(17), Article 4637. https://doi.org/10.3390/en18174637
6. Glowacz, A., et al. (2018). Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement, 113, 1–9. https://doi.org/10.1016/j.measurement.2017.08.036
7. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
8. Zheng, S., Ristovski, K., Farahat, A., et al. (2017). Long short-term memory network for remaining useful life estimation. In 2017 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 88–95). IEEE. https://doi.org/10.1109/ICPHM.2017.7998311
9. Lei, Y., Yang, B., Jiang, X., et al. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, Article 106587. https://doi.org/10.1016/j.ymssp.2019.106587
10. Mo, H., Chen, X., Guo, L., et al. (2026). A transferable digital twin-driven process design framework for high-performance multi-jet polishing. Micromachines, 17(2), Article 226. https://doi.org/10.3390/mi17020226
11. Tao, F., et al. (2018). Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 67(1), 169–172. https://doi.org/10.1016/j.cirp.2018.04.055
12. Milton, M., De La O, C., Ginn, H. L., & Benigni, A. (2020). Controller-embeddable probabilistic real-time digital twins for power electronic converter diagnostics. IEEE Transactions on Power Electronics, 35(9), 9850–9864. https://doi.org/10.1109/TPEL.2020.2971775
13. Alshathri, S., et al. (2023). Digital twin-based automated fault diagnosis in industrial IoT applications. Computers, Materials & Continua, 75(1), 183–196. DOI: 10.32604/cmc.2023.034048
14. Chinesta, F., Cueto, E., et al. (2020). Virtual, digital and hybrid twins: A new paradigm in data-based engineering and engineered data. Archives of Computational Methods in Engineering, 27, 105–134. https://doi.org/10.1007/s11831-018-9301-4
15. Karniadakis, G. E., et al. (2021). Physics-informed machine learning. Nature Reviews Physics, 3, 422–440. https://doi.org/10.1038/s42254-021-00314-5
16. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
17. Zhai, R., Yin, D., & Pang, G. (2023). A deep learning framework for solving forward and inverse problems of power-law fluids. Physics of Fluids, 35(9). https://doi.org/10.1063/5.0166323
18. Karpatne, A., Atluri, G., Faghmous, J. H., et al. (2017). Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2318–2331. https://doi.org/10.1109/TKDE.2017.2720168
19. Tsvietkov, O. V. (2025). Intelligent methods for predicting failures of power electrotechnical equipment based on multi-level digital twins. Bulletin of the Volodymyr Dahl East Ukrainian National University, 9(295), 81–90. https://doi.org/10.33216/1998-7927-2025-295-9-81-90 [in Ukrainian]
20. Melnyk, O. O., Varyvoda, Yu. Yu., & Subin, A. A. (2025). Development of digital twins for predicting technical wear of production equipment. Bulletin of Kherson National Technical University, 1(95), Part 4, 246–253. https://doi.org/10.35546/kntu2078-4481.2025.4.1.32 [in Ukrainian]
21. VanDerHorn, E., & Mahadevan, S. (2021). Digital twin: Generalization, characterization and implementation. Decision Support Systems, 145, Article 113524. https://doi.org/10.1016/j.dss.2021.113524
22. Manikantan, A. C., Joseph, R. M., Sony, A., & Pulluri, M. (2026). Digital twins in power electronics and motor drives: Architectures, challenges, and future directions. In 2026 9th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 820–826). IEEE. https://doi.org/10.1109/ICOEI68323.2026.11541733
23. Pliuhin, V., et al. (2025). The state and prospects of technical condition diagnostics methods for electric motors considering the digital twin concept. Lighting Engineering & Power Engineering, 64(2), 145–152.
24. Pengbo, Z., et al. (2025). Digital twin-assisted fault diagnosis of induction motors with thermal analysis of inter-turn short circuit faults. Measurement Science and Technology, 36(6), Article 066118.
25. Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2023). An integrated outlook of Cyber–Physical Systems for Industry 4.0: Topical practices, architecture, and applications. Green Technologies and Sustainability, 1(1), Article 100001. https://doi.org/10.1016/j.grets.2022.100001
26. Hamzah, M., Islam, M. M., Hassan, S., et al. (2023). Distributed control of cyber physical system on various domains: A critical review. Systems, 11(4), Article 208. https://doi.org/10.3390/systems11040208
27. Holtz, J. (2002). Sensorless control of induction motor drives. Proceedings of the IEEE, 90(8), 1359–1394. https://doi.org/10.1109/JPROC.2002.800726
28. Vas, P. (1998). Sensorless vector and direct torque control. Oxford University Press.
29. Peresada, S., et al. (2022). Flux linkage observer adaptive to changes in rotor active resistance of induction motors. Tekhnichna Elektrodynamika, 5, Article 45. https://doi.org/10.15407/techned2022.05.045 [in Ukrainian]
30. Lukman, G. F., & Lee, C. (2025). Towards digital twin modeling and applications for permanent magnet synchronous motors. Energies, 18(4), Article 956. https://doi.org/10.3390/en18040956
31. Friswell, M. I. (2010). Dynamics of rotating machines. Cambridge University Press.
32. Stachowiak, G. W., & Batchelor, A. W. (2025). Engineering tribology. Butterworth-Heinemann.
33. Schmid, S. R., Hamrock, B. J., & Jacobson, B. O. (2013). Fundamentals of machine elements. CRC Press.
34. Stosic, N., Smith, I. K., & Kovacevic, A. (2005). Screw compressors: Mathematical modelling and performance calculation. Springer-Verlag. https://doi.org/10.1007/3-540-26392-4
35. Boglietti, A., Cavagnino, A., Staton, D., et al. (2009). Evolution and modern approaches for thermal analysis of electrical machines. IEEE Transactions on Industrial Electronics, 56(3), 871–882. https://doi.org/10.1109/TIE.2008.2011622
Published
Issue
Section
License
Copyright (c) 2026 Energy and Automation
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All materials are disseminated under the terms of the Creative Commons Attribution 4.0 International Public License, which permits others to distribute the manuscript with proper acknowledgement of the authorship and the original publication in this journal.
