Automated Monitoring and Visualization System in Production

Vyacheslav Lyashenko
MST Department of Kharkiv National University of Radio Electronics, Kharkiv, 61166, Ukraine
Amer Tahseen Abu-Jassar
Faculty of Computer Science and Information Technology, Ajloun National University, Jordan
Vladyslav Yevsieiev
CITAR Department of Kharkiv National University of Radio electronics, Kharkiv, 61166, Ukraine
Svitlana Maksymova
CITAR Department of Kharkiv National University of Radio electronics, Kharkiv, 61166, Ukraine


Plum Analytics


In the modern world cyber-physical production systems are increasingly used. They allow you to control the flow of the technological process in production in real time. But the use of such an approach is greatly complicated by the fact that the equipment of many enterprises is old and cannot support the necessary functions. This is primarily due to the lack of the necessary sensors, as well as the corresponding software. Since the complete replacement of production equipment is very expensive, the task is to create separate monitoring systems. They must be able to integrate into the necessary parts of the production process. And they should also be cheap. In this work, we propose to build a model of such a monitoring and visualization system. The main attention in the work is focused on the hardware implementation of the proposed system and the relationship of its individual elements.


  • Industry 4/0,
  • Cyber-Physical Production System,
  • Monitoring System,
  • Visualization System,
  • Equipment Enhancement


  1. Y. Lu, Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, (2017) 1-10.
  2. M. Ghobakhloo, Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, (2020) 119869.
  3. G. Dalmarco, F.R. Ramalho, A.C. Barros, A.L. Soares, Providing industry 4.0 technologies: The case of a production technology cluster. The Journal of High Technology Management Research, 30(2), (2019) 100355.
  4. L. Silvestri, A. Forcina, V. Introna, A. Santolamazza, V. Cesarotti, Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Computers in Industry, 123, (2020) 103335.
  5. A. Raj, G. Dwivedi, A. Sharma, A.B.L. de Sousa Jabbour, & S. Rajak, Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics 224 (2020) 107546.
  6. J.M. Müller, Assessing the barriers to Industry 4.0 implementation from a workers’ perspective, IFAC-Papers Online, 52(13), (2019) 2189-2194.
  7. J. Stentoft, K.A. Wickstrom, K. Philipsen, A. Haug, Drivers and barriers for Industry 4.0 readiness and practice: empirical evidence from small and medium-sized manufacturers. Production Planning & Control, 32(10), (2021) 811-828.
  8. C. Chauhan, A. Singh, S. Luthra, Barriers to industry 4.0 adoption and its performance implications: An empirical investigation of emerging economy. Journal of Cleaner Production, 285, (2021) 124809.
  9. S. Khlamov, V. Savanevych, O. Briukhovetskyi, I. Tabakova, T.Trunova, Data Mining of the Astronomical Images by the CoLiTec Software. 6th International Conference on Computational Linguistics and Intelligent Systems, 3171(1), (2022) 1043-1055.
  10. V. Savanevych, S. Khlamov, O. Briukhovetskyi, T. Trunova, I. Tabakova, Mathematical Methods for an Accurate Navigation of the Robotic Telescopes. Mathematics, 11(10), (2023) 2246.
  11. J.H. Baker, F. Laariedh, M.A. Ahmad, V. Lyashenko, S. Sotnik, S.K. Mustafa, Some interesting features of semantic model in Robotic Science, SSRG International Journal of Engineering Trends and Technology 69(7), (2021) 38-44.
  12. Z. Sun, H. Yang, Y. Ma, X. Wang, Y. Mo, H. Li, Z. Jiang, BIT-DMR: A Humanoid Dual-Arm Mobile Robot for Complex Rescue Operations. IEEE Robotics and Automation Letters, 7(2), (2022) 802-809.
  13. S. Mellah, G. Graton, E.M. El Adel, M. Ouladsine, A. Planchais, Health State Monitoring of 4-mecanum Wheeled Mobile Robot Actuators and its Impact on the Robot Behavior Analysis Journal of Intelligent & Robotic Systems, 102, (2021) 86.
  14. J. Lee, B. Bagheri, H.A. Kao, A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, (2015) 18-23.
  15. K.L. Keung, C.K.M. Lee, L. Xia, C. Liu B. Liu P. Ji, A cyber-physical robotic mobile fulfillment system in smart manufacturing: The simulation aspect. Robotics and Computer-Integrated Manufacturing, 83, (2023) 102578.
  16. A. Villalonga, E. Negri, G. Biscardo, F. Castano, R.E. Haber, L. Fumagalli, M. Macchi, A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins. Annual Reviews in Control, 51, (2021) 357-373.
  17. T. Lins, R.A.R. Oliveira, Cyber-physical production systems retrofitting in context of industry 4.0. Computers & Industrial Engineering, 139, (2020) 106193.
  18. T. Müller, N. Jazdi, J.Ph. Schmidt, M. Weyrich, Cyber-physical production systems: enhancement with a self-organized reconfiguration management. Procedia CIRP, 99, (2021) 549-554.
  19. N. Nikolakis, R. Senington, K. Sipsas, A. Syberfeldt, S. Makris, On a containerized approach for the dynamic planning and control of a cyber – physical production system. Robotics and Computer-Integrated Manufacturing, 64, (2020) 101919.
  20. P. Loucopoulos, E. Kavakli, N. Chechina, Requirements Engineering for Cyber Physical Production Systems. In International Conference on Advanced Information Systems Engineering Cham: Springer International Publishing. 11483, (2019) 276-291.
  21. M. Andronie, G. Lăzăroiu, M. Iatagan, C. Uță, R. Ștefănescu, M. Cocoșatu, Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems, Electronics, 10(20), (2021) 2497.
  22. M.S. Khalid, V. Yevsieiev, I.S. Nevliudov, V. Lyashenko, R. Wahid, HMI Development Automation with GUI Elements for Object-Oriented Programming Languages Implementation. International Journal of Engineering Trends and Technology, 70(1), (2022) 139-145.
  23. I. Nevliudov, V. Yevsieiev, J.H. Baker, M.A. Ahmad, V. Lyashenko, and Development of a cyber design modeling declarative Language for cyber physical production systems. Journal of Mathematical and Computational Science, 11(1), (2020) 520-542.
  24. I. Nevliudov, V. Yevsieiev, V. Lyashenko, M.A. Ahmad, GUI Elements and Windows Form Formalization Parameters and Events Method to Automate the Process of Additive Cyber-Design CPPS Development, Advances in Dynamical Systems and Applications (ADSA), 16(2), (2021) 441-455.
  25. I. Nevliudov, M. Omarov, V. Yevsieiev, A. Bronnikov, V. Lyashenko, Method of Algorithms for Cyber-Physical Production Systems Functioning Synthesis, International Journal of Emerging Trends in Engineering Research, 8(10), (2020) 7465-7473.
  26. R.G. Lins, P.R.M. de Araujo, M. Corazzim. In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems. Robotics and Computer-Integrated Manufacturing, 61, (2020) 101859.
  27. D.S. Icon, J.F. Arinez, M.T. Collins, Zh. Bi, Modelling of human–machine interaction in equipment design of manufacturing cells, Journal Enterprise Information Systems, 11(7), (2017) 969-987.



Article Details

Volume 5, Issue 6, Year 2023

Published 2023-10-10


Download data is not yet available.