Methods for data mining and natural language processing in the management of robotic production systems

DOI: 10.21293/1818-0442-2023-26-3-65-71

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Abstract: The paper presents a comparative analysis of natural language processing and data mining methods used in data processing in the industrial robotic systems. A concept for implementing the process of integrating artificial intelligence methods into production management systems has been developed, and the necessary components have been described. The focus is on the use of natural language processing methods. The work could be useful for conducting experimental research in the field of application of natural language processing and artificial intelligence methods in robotic production systems.

Keywords: smart factories, machine learning, big data, robotic production systems, natural language processing, text mining, natural lan-guage processing, cybersecurity

Funding: This work was supported by the Ministry of Science and Higher Education of the Russian Federation, No. 2019-0898.

For citation:
Vorobieva A. A., Fedosenko M. Yu. Methods for data mining and natural language processing in the management of robotic production systems. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2023, vol. 26, no. 3, pp. 65–71. DOI: 10.21293/1818-0442-2023-26-3-65-71

Authors and copyright holders:

  • Vorobieva A. A. , National-Research-Itmo-University-(st.-Petersburg,-Russia)
  • Fedosenko M. Yu. , National-Research-Itmo-University-(st.-Petersburg,-Russia)

  • 1. How natural language processing helps manufacturing sector? Available at: https://www.analyticsinsight.net/how-natural-language-processing-helps-manufacturing-sector (Accessed: November 01.11.2022).
  • 2. Zenkert J., Weber С., Dornhöfer M., Abu-Rasheed H., Fath M. Knowledge Integration in Smart Factories, Encyclopedia, 2021, vol. 1, no. 3, pp. 792–811.
  • 3. Rabelo R.J., Romero D., Zambiasi S.P. Softbots supporting the operator 4.0 at smart factory environments, IFIP International Conference on Advances in Production Management Systems, 2018, pp. 456–464.
  • 4. Strozzi F., Colicchia C., Creazza A., Noè C. Literature review on the ‘Smart Factory’ concept using bibliometric tools, International Journal of Production Research, 2017, vol. 55, no. 22, pp. 6572–6591.
  • 5. What is an engineering change request (ECR)? Available at: https://www.arenasolutions.com/resources/articles/engineering-change-request/ (Accessed: November 01.11.2022).
  • 6. Wickel M.C., Lindemann U. How to integrate information about past engineering changes in new change processes? Proceedings of the 20th International Conference on Engineering Design (ICED 15), 2015, vol. 3, pp. 229–238.
  • 7. Sharafi A. Knowledge discovery in databases. Springer Fachmedien Wiesbaden, 2013, pp. 51–108.
  • 8. A posteriori design change analysis for complex engineering projects. Available at: https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/133/10/101005/467343/A-Posteriori-Design-Change-Analysis-for-Complex (Accessed: 02.11. 2023).
  • 9. Change propagation analysis in complex technical systems. Available at: https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/131/8/081001/466974/Change-Propagation-Analysis-in-Complex-Technical (Accessed: 02.11.2023).
  • 10. Modeling engineering change management process in virtual collaborative design environments. Available at: https://spectrum.library.concordia.ca/id/eprint/9094/ (Accessed: 02.11.2023).
  • 11. Knowledge-based methods for evaluation of engineering changes. Available at: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/78926/mehtacr_1.pdf?sequence=1 (Accessed: 02.11.2023).
  • 12. Arnarsson I.O. Systematic Analysis of Engineering Change Request Data: Applying Data Mining Tools to Gain New Fact-Based Insights. Sweden, Goteborg, Chalmers Tekniska Hogskola, 2020. 80 p.
  • 13. Natural Language Processing for Manufacturing Industry. Available at: https://www.stridelysolutions.com/resources/blog/natural-language-processing-for-manufacturing-industry (Accessed: 01.11.2022).
  • 14. Grieco A., Pacella M., Blaco M. On the application of text clustering in engineering change process, Procedia CIRP, 2017, vol. 62, pp. 187–192.
  • 15. Kohonen T. Self-Organizing Maps, Artificial Neural Networks (Third Extended Edition), New York, 2001,501 p.
  • 16. DEVOPEDIA for developers. by developers. Available at: https://devopedia.org/images/article/187/4433.1560446395.png (Accessed: 01.11.2022).
  • 17. Microgrid cyber-security: Review and challenges toward resilience. Available at: https://www.mdpi.com/2076-3417/10/16/5649 (Accessed: 01.11.2023).
  • 18. Gimenez-Aguilar M., de Fuentes J.M., Gonzalez-Manzano L., Arroyo D. Achieving cybersecurity in blockchain-based systems: A survey, Future Generation Computer Systems, 2021, vol. 124, pp. 91–118.
  • 19. Anthi E., Williams L., Rhode M., Burnap P., Wedgbury A. Adversarial attacks on machine learning cybersecurity defenses in industrial control systems, Journal of Information Security and Applications, 2021, vol. 58, P. 102717.
  • 20. Wiegers K., Beatty J. Software requirements, Pearson Education, 2013, 576 p.
  • 21. Wickel M.C., Lindemann U. A retrospective analysis of engineering change orders to identify potential for future improvements, Proceedings of NordDesign, 2014, pp. 692–701.
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