Application of predictive maintenance in the packaging production

• Autorzy: Palchevskiy Bogdan , Krestyanpol Lyubov

Identyfikatory

DOI

10.35784/iapgos.3051

Warianty tytułu

PL

Zastosowanie predykcyjnej diagnostyki w produkcji opakowań

• Języki publikacji: EN

Abstrakty

EN

To solve the problem of predictive maintenance for packaging manufacturing, we propose a hybrid model that optimizes the maintenance plan. The model is based on monitoring the state of many components of a multi-position automatic packaging machine and makes it possible to predict their future malfunctions and estimate the remaining service life of the equipment. The effectiveness of the proposed solution is demonstrated with the help of a real industrial multi-position machine for the automatic production of film bags and packaging of paste in them. The methodology is based on the analysis of diagnostic information using an expert system.

PL

Aby rozwiązać problem predykcyjnego utrzymania ruchu w produkcji opakowań, proponujemy hybrydowy model optymalizujący plan utrzymania ruchu. Model ten opiera się na monitorowaniu stanu wielu komponentów wielostanowiskowej automatycznej maszyny pakującej i umożliwia przewidywanie ich przyszłych awarii oraz szacowanie pozostałego czasu eksploatacji urządzenia. Skuteczność proponowanego rozwiązania została zademonstrowana na przykładzie rzeczywistej przemysłowej maszyny wielostanowiskowej do automatycznej produkcji torebek foliowych i pakowania w nie pasty. Metodyka opiera się na analizie informacji diagnostycznych z wykorzystaniem systemu eksperckiego.

Słowa kluczowe

EN

technological equipment; expert system; monitoring; diagnostics; intelligent control system

PL

aparatura technologiczna; system ekspertowy; monitoring; diagnostyka; inteligentny system sterowania

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2022
• Tom: T. 12, nr 3
• Strony: 27--33
• Opis fizyczny: Bibliogr. 21 poz., tab., rys., wykr.

Twórcy

autor

Palchevskiy Bogdan

• Lutsk National Technical University, Faculty of Computer and Information Technologies, Lutsk, Ukraine

• https://orcid.org/0000-0002-4000-4992

autor

Krestyanpol Lyubov

• Lesya Ukrainka Eastern European National University, Faculty of Foreign Linguistics, Lutsk, Ukraine

• https://orcid.org/0000-0003-3617-7900

Bibliografia

• [1] Becker T., Wagner D.: Identification of Key Machines in Complex Production Networks. Procedia CIRP 41, 2016, 69–74 [http://doi.org/10.1016/j.procir.2015.12.006].
• [2] Chaoyang G., Fenli G.: Embedded fault diagnosis expert system on weapon equipment. International Journal of Advanced Network, Monitoring and Controls 1(2), 2016, 25–33 [http://doi.org/10.2991/mcei-16.2016.234].
• [3] Dekker R. et al.: A review of multi-component maintenance models with economic dependence. Mathematical Methods of Operations Research 45, 1997, 411–435 [http://doi:10.1007/bf01194788].
• [4] Erik L. J.: Expert system for diagnosing computer numerically controlled machines: a case-study. Computers in Industry 32(3), 1997, 233–248 [http://doi.org/10.1016/S0166-3615(96)00077-2].
• [5] He Q., Li X. Q.: Management of knowledge base of expert system for fault diagnosis of rotating machinery. Applied Mechanics and Materials 44–47, 2010, 2935–2939 [http://doi.org/10.4028/www.scientific.net/AMM.44-47.2935].
• [6] IEC 60300-3-1:2003, Dependability management – Part 3-1: Application guide – Analysis techniques for dependability – Guide on methodology.
• [7] ISO 17359:2003(E), Condition monitoring and diagnostics of machines – General guidelines.
• [8] Lee J., Bagheri B., Kao H.-A.: A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters 3, 2015, 18–23 [http://doi.org/10.1016/j.mfglet.2014.12.001].
• [9] Majstorovic V. D.: Expert systems for diagnosis and maintenance: The state-of-the-art. Computers in Industry 15(1–2), 1990, 43–68 [http://doi.org/10.1016/0166-3615(90)90084-3].
• [10] Mobley R. K.: An Introduction to Predictive Maintenance. Elsevier Science, 2002.
• [11] Mourtzis D., Vlachou E., Milas N.: Industrial Big Data as a Result of IoT Adoption in Manufacturing. Procedia CIRP 55, 2016, 290–295 [http://doi.org/10.1016/j.procir.2016.07.038].
• [12] Olatunbosun A., Arulogunol O.: An expert system based equipment diagnostics. Journal of Applied Science, Engineering and Technology 5, 2005, 63–70 [http://doi.org/10.4314/jaset.v5i1.38299].
• [13] Palchevskyi B., Krestyanpol L.: The Use of the “Digital Twin” Concept for Proactive Diagnosis of Technological Packaging Systems. Babichev S., Peleshko D., Vynokurova O. (eds): Data Stream Mining & Processing. Communications in Computer and Information Science 1158, Springer, Cham. 2020 [http://doi.org/10.1007/978-3-030-61656-4_29].
• [14] Palchevskyi B., Krestyanpol L.: Strategy of Construction of Intellectual Production Systems. IEEE Third International Conference on Data Stream Mining & Processing (DSMP), 2020, 362–365 [http://doi.org/10.1109/DSMP47368.2020.9204190].
• [15] Palchevskyi B.: Improving the efficiency of intelectual packaging systems. Technological complexes 15, 2018, 4–14.
• [16] Sang G. M., Xu L., de Vrieze P.: Simplifying Big Data analytics systems with a reference architecture. Camarinha-Matos L., Afsarmanesh H., Fornasiero R. (eds): Collaboration in a Data-Rich World. PRO-VE 2017. IFIP Advances in Information and Communication Technology 506. Springer, Cham. [http://doi.org/10.1007/978-3-319-65151-4_23].
• [17] Tobon-Mejiaab D. A., Medjahera K., Zerhounia N.: CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks. Mechanical Systems and Signal Processing 28, 2012, 167–182 [http://doi.org/10.1016/j.ymssp.2011.10.018].
• [18] Van Horenbeek A., Pintelon L., Muchiri P.: Maintenance optimization models and criteria. International Journal of System Assurance Engineering and Management 1, 2010, 189–200 [http://doi:10.1007/s13198-011-0045-x].
• [19] Wang H.: A survey of maintenance policies of deteriorating systems. European Journal of Operational Research.Volume 139(3), 2002, 469–489 [http://doi.org/10.1016/S0377-2217(01)00197-7].
• [20] Wu W., Hu J., Zhang J.: Prognostics of Machine Health Condition Using an Improved ARIMA-Based Prediction Method. Second IEEE Conference on Industrial Electronics and Applications ICIEA, 2007, 1062–1067 [http://doi.org/10.1109/iciea.2007.4318571].
• [21] Zhang Z., Li Z., Zhao C.: Research on condition monitoring and fault diagnosis of intelligent copper ball production lines based on big data. IET Collaborative Intelligent Manufacturing 4(1), 2022, 45–57 [http://doi.org/10.1049/cim2.12043].

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).