Mathematical models ontology of technological objects for industrial enterprises. Part 1. formation of the basic concepts for the subject area

Authors

DOI:

https://doi.org/10.31548/energiya1(65).2023.023

Abstract

The article substantiates the necessity of constructing ontologies of mathematical models for technological objects of industrial enterprises. For this, a survey was conducted and expert evaluations were obtained, which showed that there is currently no complete classification of existing mathematical models and corresponding ontologies in the field of industry. Experts also noted that the presence of such an ontology significantly facilitated their work in conducting research and work related to the creation of highly efficient production management systems based on models. Such models will include existing mathematical representations of technological processes, as well as methods for identifying their parameters. Based on the results of expert evaluations, Ishikawa’ diagram was constructed, which reflects the factors affecting the development of a mathematical model and is the basis for the development of an ontology. Also, to create an adequate ontology, the place of the mathematical model in the hierarchy of existing models is determined. An important stage in the design of the ontology was the classification of existing mathematical models according to selected characteristics, which included the structure of the model, its character, its object properties, the purpose of the model and mathematical dependencies. The main concepts of the models are defined, which include classic and modern varieties of models for technological processes.

Key words: mathematical model, ontology, concept, technological process, relationship, automation

References

Smart Manufacturing – Reference Architecture Model Industry 4.0 (RAMI 4.0). Available at: https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/Details_of_the-_Asset_Administration_Shell_Part1_V3.html.

Illa, N., Sin, T. C., Fathullah, G. M., Rosmaini, A. (2018). Mathematical modeling of quality and productivity in industries: A review. AIP Conference Proceedings November 2018, 2030(1):020126. doi:10.1063/1.5066767.

Zaiets, N., Vlasenko, L., Lutska, N., Shtepa, V. (2022). Resource Efficiency Forecasting Neural Network Model for the Sugar Plant Diffusion Station. Automation 2022: New Solutions and Technologies for Automation, Robotics and Measurement Techniques, 151–161. doi:10.1007/978-3-031-03502-9_16.

Ljung, L. (1999). System identification: theory for the user. Prentice Hall PTR. 609.

Gresova, E., Svetlík, J. (2021). Mathematical modeling of the manufacturing sector’s dominant part as a base for automation. Applied Sciences, 11, 3295. doi:10.3390/app11073295.

Korobiichuk, I., Ladanyuk, A., Vlasenko, L., Zaiets, N. (2018). Modern Development Technologies and Investigation of Food Production Technological Complex Automated Systems. Proceedings of 2-nd International Conference on Mechatronics Systems and Control Engineering ICMSCE 2018. Amsterdam, Nitherlands, 52–56. doi:10.1145/3185066.3185075.

Boss, B., et al. (2020). Digital twin and Asset Administration Shell Concepts and Application in the Industrial Internet and Industrie 4.0, an Industrial Internet Consortium and Plattform Industrie 4.0 Joint. White Paper.

Akroyd, J., Mosbach, S., Bhave, A., Kraft, M. (2021). Universal digital twin – a dynamic knowledge graph. Data-Centric Engineering, 2, e14. doi:10.1017/dce.2021.10.

D’Amico, R.D., Sarkar, A., Karray, H., Addepalli, S., Erkoyuncu, J.A. (2022). Detecting failure of a material handling system through a cognitive twin. IFAC-PapersOnLine, 55 (10), 2725–2730. doi:10.1016/j.ifacol.2022.10.128.

ISO 20534: 2018 Industrial Automation Systems and Integration – Formal Semantic Models for the Configuration of Global Production Networks. Available at: https://www.iso.org/standard/68274.html.

Gal, A. Ontology Engineering. (2009). Encyclopedia of Database Systems. Springer, Boston, MA. doi:10.1007/978-0-387-39940-9_1315.

Lutskaya, N., Vlasenko, L., Zaiets, N., Shtepa, V. (2021). Ontological aspects of developing robust control systems for technological objects. ICO 2020: Intelligent Computing and Optimization, Advances in Intelligent Systems and Computing book series, 1324, 1252– 1261. doi:10.1007/978-3-030-68154-8_107.

Vlasenko, L. O., Lutska, N. M., Zaiets, N. A., Shyshak, A. V., Savchuk, O. V. (2022). Domain ontology development for condition monitoring system of industrial control equipment and devices. Radio Electronics, Computer Science, Control, 1, 157. doi:10.15588/1607-3274-2022-1-16.

Wei, S., Yu, Z., Chen, F., Mao, C., Guo, J. (2015). The Expert Ranking Method Based on Listwise with Associated Features. In: Zhang, X., Sun, M., Wang, Z., Huang, X. (eds) Social Media Processing. SMP 2015. Communications in Computer and Information Science, 568. Springer, Singapore. doi:10.1007/978-981-10-0080-5_18

Zhang, T., Harrington, K. B., Sherf, E. N. (2022). The errors of experts: When expertise hinders effective provision and seeking of advice and feedback. Current Opinion in Psychology, 43, 91-95. doi:10.1016/j.copsyc.2021.06.011.

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Published

2023-04-26

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No. 1 (2023)

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Статті

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