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The priority of resources in the management system of influence on the system of technical operation of machines was evaluated. The proposed systemological model of information support for making managerial decisions regarding the technical operation of machines. The given analogy between the factors of technical influence and the resources required for this is substantiated. These are: "action" - "human resource"; "means" - "material resource"; "environment" - "information resource". The priority of the information resource in ensuring the efficiency of the technical operation of the machines is determined by expert evaluation methods. The proposed systemological model consists of the structuring and systematization of information resources of dispatch reports and the procedure for performing further analytical procedures performed by information and analytical maintenance of enterprises with the help of software to obtain relevant information. Such continuous monitoring of equipment operation processes provides engineers with the necessary data for: the analysis and selection of an effective model of technical operation of the equipment; to develop alternative management decisions and make the optimal one; development of individual models and maintenance strategies with their adjustment and adaptation to real operating conditions. Also, this model provides professionals with a tool for comprehensive evaluation of the efficiency of the enterprise's production organization, the dynamics of its development, and the consequences of management decisions in different periods. It makes it possible to make informed decisions regarding the improvement of the management system and the subsequent formation of an effective strategy for the technical operation of machines.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
337--347
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
autor
- Yuriy Kondratyuk Poltava Polytechnic National University, Poltava, Ukraine
autor
- Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine
autor
- LLC «Ekspertnaftogaz», Poltava, Ukraine
autor
- Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine
autor
- AGH University of Science and Technology, Krakow, Poland
Bibliografia
- 1. Anh, D. T., Skrzypek, K., 2018. The predictive maintenance concept in the maintenance department of the “Industry 4.0” production enterprise. Foundations of Management, 10(1), 283-292, DOI:10.2478/fman-2018-0022
- 2. Barré, O., Napame, B., 2017. The insulation for machines having a high lifespan expectancy, design, tests and acceptance criteria issues. Machines, 5(1), 7. DOI:10.3390/machines5010007
- 3. Bousdekis, A., Lepenioti, K., Apostolou, D., Mentzas, G. 2019. Decision making in predictive maintenance: literature review and research agenda for industry 4.0. IFAC-Papers OnLine, 52(13), 607-612. DOI:10.1016/j.ifacol.2019.11.226
- 4. Buchynskyi, M., Buchynskyi, M. Ya. , Vasylchenko, M. I., 2022. Forecasting the technical efficiency of mobile workover rigs. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. (5): 033-038. DOI:10.33271/nvngu/2022-5/033.
- 5. Chiaka Okereke E. 2019. Equipment maintenance strategies on the viability of beverage industries in South-East Nigeria. Strategic Journal of Business and Social Science (SJBSS) #2, 105 pages. Retrieved from www.sj-bss.com.
- 6. Dhillon, B.S. 2019 Engineering maintenance: a modern approach. Plant maintenance - Management. CRC Press LLC. eBook ISBN9780429132209. DOI:10.1201/9781420031843
- 7. Douglas, S. T., 2018. The Costs and Benefits of Advanced Maintenance in Manufacturing. Advanced Manufacturing Series (NIST AMS)- 100-18. DOI:10.6028/NIST.AMS.100-18.
- 8. Henriquez, P., Alonso, J. B., Ferrer, M. A., Travieso, C. M. 2013. Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(5), 642-652. DOI:10.1109/TSMCC.2013.2257752
- 9. Holomovzyi, V., Kalynovska, N. 2020. Analysis of maintenance and repair systems and the effectiveness of their influence on the technical condition of the factory system of machines during its operation. Ekonomika ta derzhava, 7, 124-128. DOI:10.32702/2306-6806.2020.7.124.
- 10. Hu, J., Jiang, Z., Liao, H. 2017. Preventive maintenance of a single machine system working under piecewise constant operating condition. Reliability Engineering & System Safety, 168, 105-115. DOI:10.1016/j.ress.2017.05.014
- 11. Jasiulewicz-Kaczmarek, M., Legutko, S., Kluk, P. (2020). Maintenance 4.0 technologies-new opportunities for sustainability driven maintenance. Management and production engineering review, DOI:10.24425/mper.2020.133730
- 12. Jeong, D., Shane, J., Scheibe, K., Nilakanta, S., Alikhani, A., 2019. Optimizing maintenance equipment life-cycle for local agencies. A report from Institute for Transportation Iowa State University (IHRB Project TR-727). Retrieved from www.intrans.iastate.edu.
- 13. Kiyanovskyy, M., Dubrovskiy, S. 2016. The TOiR equipment quality of the GOK. Mining Journal, 101, 126-130. Retrieved from http://iomining.in.ua/ua/homeua/journal/101ua/.
- 14. Kyi K. S, Zar, C. T., Kyaw M. M., 2019. Maintenance Management Plan of Heavy Machinery. Iconic Research and Engineering Journals. #2, 28-36. Retrieved from https://www.academia.edu/43475688/Mainte-nance_Management_Plan_of_Heavy_Machinery
- 15. Lundgren, C., Skoogh, A., Bokrantz, J., 2018. Quantifying the Effects of Maintenance - a Literature Review of Maintenance Models. Procedia CIRP, 72: 1305-1310. DOI:10.1016/j.procir.2018.03.175.
- 16. Matin, S. A. A., Mansouri, S. A., Bayat, M., Jordehi, A. R., Radmehr, P., 2022. A multi-objective bi-level optimization framework for dynamic maintenance planning of active distribution networks in the presence of energy storage systems. Journal of Energy Storage, 52, 104762. DOI:10.1016/j.aei.2023.102011
- 17. Mehmeti, Xh., Mehmeti, B., Sejdiu., Rr., 2018. The equipment maintenance management in manufacturing enterprises. IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. 51-30, 800-802 DOI:10.1016/j.ifacol.2018.11.192.
- 18. Naji, A., Beidouri, Z., Oumami, M.,, Bouksour, O., 2016. Maintenance management and innovation in industries: a survey of Moroccan companies. International Journal of Innovation, vol. 4, núm. 2, Retrieved from https://www.redalyc.org/articulo.oa?id=499151080014.
- 19. Nurprihatin, F., Angely, M., Tannady, H., 2019. Total productive maintenance policy to increase effectiveness and maintenance performance using overall equipment effectiveness. Journal of applied research on industrial engineering, 6(3), 184-199.
- 20. Oliinyk, A., Velboi, M., Lukianovets, N., 2021. The role of personnel and production costs in effective management of the enterprise. Agrosvit, vol. 7-8, 94-102. DOI: 10.32702/2306-6792.2021.7-8.94
- 21. Pant, H., Singh, S. B., 2022. Availability and cost assessment of systems with dormant failure undergoing sequential inspections. Journal of Quality in Maintenance Engineering, 28(3), 533-544. DOI: 10.1108/JQME-10-2020-0112
- 22. Polenghi, A., Roda, I., Macchi, M., Pozzetti, A., 2022. Information as a key dimension to develop industrial asset management in manufacturing. Journal of Quality in Maintenance Engineering, 28(3), 567-583. DOI:10.1108/JQME-09-2020-0095
- 23. Quatrini, E., Costantino, F., Di Gravio, G., Patriarca, R., 2020. Condition-based maintenance - an extensive literature review. Machines, 8(2), 31. DOI:10.3390/machines8020031
- 24. Semenov S.S. Otsenka kachestva y tekhnycheskoho urovnia slozhnykh system: Praktyka prymenenyia metoda ekspertnykh otsenok. - M.: LENAND, 2015
- 25. Shaheen, B. W., Németh, I., 2022. Integration of Maintenance Management System Functions with Industry 4.0 Technologies and Features-A Review. Processes, 10(11), 2173. DOI:10.3390/pr10112173
- 26. Shin, J. H., Jun, H. B. 2015. On condition based maintenance policy. Journal of Computational Design and Engineering, 2(2), 119-127. DOI:10.1016/j.jcde.2014.12.006
- 27. Sobaszek, Ł., Gola, A., Świć, A., 2020. Time-based machine failure prediction in multi-machine manufacturing systems. Eksploatacja i Niezawodność - Maintenance and Reliability, 22(1), 52-62. DOI:10.17531/ein.2020.1.7.
- 28. Stenström, C., Norrbin, P., Parida, A., Kumar, U., 2016. Preventive and corrective maintenance–cost comparison and cost-benefit analysis. Structure and Infrastructure Engineering, 12(5), 603-617. DOI:10.1080/15732479.2015.1032983
- 29. Vogl, G. W., Weiss, B. A., Helu, M., 2019. A review of diagnostic and prognostic capabilities and best practices for manufacturing. Journal of Intelligent Manufacturing, 30, 79-95. DOI: 10.1007/s10845-016-1228-8
- 30. Xhemajl, M., Besart, M., Rrahim, S., 2018. The equipment maintenance management in manufacturing enterprises. IFAC-Papers OnLine 51(30), 800-802. DOI:10.1016/j.ifacol.2018.11.192.
- 31. Xiao, L., Song, S., Chen, X., Coit, D. W., 2016. Joint optimization of production scheduling and machine group preventive maintenance. Reliability Engineering & System Safety, 146, 68-78. DOI:10.1016/j.ress.2015.10.013
- 32. Zhang, C., Zhang, Y., Dui, H., Wang, S., Tomovic, M. M. 2022. Importance measure-based maintenance strategy considering maintenance costs. Eksploatacja i Niezawodność-Maintenance and Reliability, 24(1). DOI:10.17531/ein.2022.1.3.
- 33. Zhu, H., Liu, F., Shao, X., Liu, Q., Deng, Y., 2011. A cost‐based selective maintenance decision‐making method for machining line. Quality and Reliability Engineering International, 27(2), 191-201.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bbd4b88b-0233-48d7-ad3a-e52fcd21ba7e