DOI: https://doi.org/10.32515/2664-262X.2024.10(41).2.3-10

Algorithm for Optimizing the Reliability of Operation and Efficiency of Use of Production Equipment Using Artificial Intelligence Methods

Оleksandr Revniuk, Nataliya Zagorodnа, Oleksandr Ulichev

About the Authors

Оleksandr Revniuk, post-graduate, Ivan Pulyuy Ternopil National Technical University, Ternopil, Ukraine, e-mail: revo0708@gmail.com, ORCID ID: 0009-0005-0511-5354

Nataliya Zagorodnа, Associate Professor, PhD in Pedagogicals (Candidate of Pedagogical Sciences), Ivan Pulyuy Ternopil National Technical University, Ternopil, Ukraine, e-mail: , ORCID ID: 0000-0002-1808-835X

Oleksandr Ulichev, PhD in Technics (Candidate of Technics Sciences), Central Ukraіnian National Technical University, Kropyvnytskyi, Ukraine, e-mail: askin79@gmail.com, ORCID ID: 0000-0003-3736-9613

Abstract

This paper presents an innovative approach that involves the optimization of maintenance for production equipment by leveraging advanced artificial intelligence (AI) algorithms. The study explores the application of Markov process theory within the context of reinforcement learning and its integration into the modeling of production environments. The focus of this research is to address critical issues related to enhancing the reliability and efficiency of production processes. This is achieved by reducing maintenance costs and minimizing equipment downtime. The proposed model for optimizing the use of the production equipment system is described as an environment with discrete states, where an agent operates with the capability to perform specific actions. This model emphasizes the implementation of the Q-learning algorithm, a form of reinforcement learning that aims to optimize production processes by enabling the agent to learn and make decisions that enhance system performance. Through the iterative process of Q-learning, the agent evaluates the potential benefits of various actions in different states, gradually refining its strategy to maximize long-term rewards. Q-learning, with its ability to handle environments with unknown dynamics, is particularly effective in this context. It helps the agent to develop an optimal maintenance policy by balancing immediate maintenance costs against the long-term benefits of reduced downtime and extended equipment lifespan. The iterative nature of Q-learning ensures continuous improvement and adaptation, making the system robust against varying operational conditions and unforeseen disruptions. Through rigorous analysis and experimentation, the findings demonstrate a significant improvement in the reliability and productivity of the production equipment system. The introduction of AI technologies, specifically through the Q-learning framework, not only streamlines maintenance practices but also ensures a more efficient utilization of resources, ultimately leading to a more robust and cost-effective production environment. The results of this study highlight the potential of AI in transforming traditional production maintenance strategies and setting new standards for operational excellence.

Keywords

productionsystem, productionequipment, artificialintelligence, Markovprocesses, reliabilityoffunctioning, efficiencyofuse

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References

1. Zahid A. (2024) Vulnerability detection and prevention: an approach to enhance cybersecurity. MS Computer Science. https://doi.org/10.13140/RG.2.2.31687.71841

2. Humayun. M., Niazi. M., & Jhanjhi. N. (2020). Cyber Security Threats and Vulnerabilities: A Systematic Mapping Study / M. Humayun et al. Arabian Journal for Science and Engineering. (Vol. 45(4)). (pp 3171–3189). https://doi.org/10.1007/s13369-019-04319-2.

3. Asaduzzaman M. (2020). Security Aspects of ePayment System and Improper Access Control in Microtransactions. EasyChair.

4. (2024) Data Breach Investigations Report. Verizon Business. https://www.verizon.com/business/resources/reports/2024-dbir-data-breach-investigations-report.pdf.

5. Lella I., Theocharidou M., Magonara E. (2024). Enisa threat landscape. ENISA, 2024.

6. Ravindran U., Potukuchi R. V. (2022). A review on web application vulnerability assessment and penetration testing. Review of Computer Engineering Studies. (Vol. 9(1)). https://doi.org/ 10.18280/rces.090101

7. Pentest monkey. https://pentestmonkey.net/

8. I. Yaqoob, S.A. Hussain, & S. Mamoon. (2017) Penetration Testing and Vulnerability Assessment . Journal of Network Communications and Emerging Technologies (JNCET). 2017. (Vol. 7(8)).

9. N. Rane, & A. Qureshi. (2024). Comparative Analysis of Automated Scanning and Manual Penetration Testing for Enhanced Cybersecurity. 12th International Symposium on Digital forensics and security : Conference. San Antonio.

10. OWASP Foundation, the Open Source Foundation for Application Security. https://owasp.org

11. National Institute of Standards and Technology. https://www.nist.gov

12. Cyber Security Training. https://www.sans.org/emea

13. A. van der Stock, D. Cuthbert, & J. Manico. (2021). OWASP Application Security Verification Standard 4.0.3.

14. CWE - Common Weakness Enumeration. https://cwe.mitre.org.

Citations

1. Zahid A. Vulnerability detection and prevention: an approach to enhance cybersecurity. MS Computer Science. 2024. DOI: 10.13140/RG.2.2.31687.71841

2. Humayun. M., Niazi, M., Jhanjhi. N. Cyber Security Threats and Vulnerabilities: A Systematic Mapping Study / M. Humayun et al. Arabian Journal for Science and Engineering. 2020. T. 45(4). 3171–3189. DOI: 10.1007/s13369-019-04319-2.

3. Asaduzzaman M. Security Aspects of ePayment System and Improper Access Control in Microtransactions. EasyChair. 2020.

4. 2024 Data Breach Investigations Report. Verizon Business. URL: https://www.verizon.com/business/resources/reports/2024-dbir-data-breach-investigations-report.pdf.

5. Lella I., Theocharidou M., Magonara E. Enisa threat landscape 2024. ENISA, 2024. 130 с.

6. Ravindran U., Potukuchi R. V. A review on web application vulnerability assessment and penetration testing. Review of Computer Engineering Studies. 2022. Т. 9, № 1. С. 1–22. DOI: 10.18280/rces.090101

7. Pentest monkey. URL: https://pentestmonkey.net/

8. I. Yaqoob, S.A. Hussain, S. Mamoon. Penetration Testing and Vulnerability Assessment. Journal of Network Communications and Emerging Technologies (JNCET). 2017. Т. 7, № 8.

9. N. Rane, A. Qureshi. Comparative Analysis of Automated Scanning and Manual Penetration Testing for Enhanced Cybersecurity. 12th International Symposium on Digital forensics and security : : матеріали конференції, м. San Antonio, 29 квіт. 2024 р. San Antonio, 2024.

10. OWASP Foundation, the Open Source Foundation for Application Security. URL: https://owasp.org

11. National Institute of Standards and Technology. URL: https://www.nist.gov

12. Cyber Security. URL: https://www.sans.org/emea

13. A. van der Stock, D. Cuthbert, J. Manico. OWASP Application Security Verification Standard 4.0.3. 2021. 71 с.

14. CWE - Common Weakness Enumeration. URL: https://cwe.mitre.org.

Copyright (c) 2024 Оleksandr Revniuk, Nataliya Zagorodnа, Oleksandr Ulichev