APPLICATION OF PREDICTIVE MAINTENANCE IN THE PACKAGING PRODUCTION

Bogdan Palchevskyi

bogdan_pal@ukr.net
Lutsk National Technical University (Ukraine)
http://orcid.org/0000-0002-4000-4992

Lyubov Krestyanpol


Lesya Ukrainka Eastern European National University (Ukraine)
http://orcid.org/0000-0003-3617-7900

Abstract

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.


Keywords:

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

Becker T., Wagner D.: Identification of Key Machines in Complex Production Networks. Procedia CIRP 41, 2016, 69–74.
DOI: https://doi.org/10.1016/j.procir.2015.12.006   Google Scholar

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.
  Google Scholar

Dekker R. et al.: A review of multi-component maintenance models with economic dependence. Mathematical Methods of Operations Research 45, 1997, 411–435.
DOI: https://doi.org/10.1007/BF01194788   Google Scholar

Erik L. J.: Expert system for diagnosing computer numerically controlled machines: a case-study. Computers in Industry 32(3), 1997, 233–248.
DOI: https://doi.org/10.1016/S0166-3615(96)00077-2   Google Scholar

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.
DOI: https://doi.org/10.4028/www.scientific.net/AMM.44-47.2935   Google Scholar

IEC 60300-3-1:2003, Dependability management – Part 3-1: Application guide – Analysis techniques for dependability – Guide on methodology.
  Google Scholar

ISO 17359:2003(E), Condition monitoring and diagnostics of machines – General guidelines.
  Google Scholar

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.
DOI: https://doi.org/10.1016/j.mfglet.2014.12.001   Google Scholar

Majstorovic V. D.: Expert systems for diagnosis and maintenance: The state-of-the-art. Computers in Industry 15(1–2), 1990, 43–68.
DOI: https://doi.org/10.1016/0166-3615(90)90084-3   Google Scholar

Mobley R. K.: An Introduction to Predictive Maintenance. Elsevier Science, 2002.
DOI: https://doi.org/10.1016/B978-075067531-4/50006-3   Google Scholar

Mourtzis D., Vlachou E., Milas N.: Industrial Big Data as a Result of IoT Adoption in Manufacturing. Procedia CIRP 55, 2016, 290–295.
DOI: https://doi.org/10.1016/j.procir.2016.07.038   Google Scholar

Olatunbosun A., Arulogunol O.: An expert system based equipment diagnostics. Journal of Applied Science, Engineering and Technology 5, 2005, 63–70.
DOI: https://doi.org/10.4314/jaset.v5i1.38299   Google Scholar

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.
DOI: https://doi.org/10.1007/978-3-030-61656-4_29   Google Scholar

Palchevskyi B., Krestyanpol L.: Strategy of Construction of Intellectual Production Systems. IEEE Third International Conference on Data Stream Mining & Processing (DSMP), 2020, 362–365.
DOI: https://doi.org/10.1109/DSMP47368.2020.9204190   Google Scholar

Palchevskyi B.: Improving the efficiency of intelectual packaging systems. Technological complexes 15, 2018, 4–14.
  Google Scholar

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.
DOI: https://doi.org/10.1007/978-3-319-65151-4_23   Google Scholar

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.
DOI: https://doi.org/10.1016/j.ymssp.2011.10.018   Google Scholar

Van Horenbeek A., Pintelon L., Muchiri P.: Maintenance optimization models and criteria. International Journal of System Assurance Engineering and Management 1, 2010, 189–200.
DOI: https://doi.org/10.1007/s13198-011-0045-x   Google Scholar

Wang H.: A survey of maintenance policies of deteriorating systems. European Journal of Operational Research.Volume 139(3), 2002, 469–489.
DOI: https://doi.org/10.1016/S0377-2217(01)00197-7   Google Scholar

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.
DOI: https://doi.org/10.1109/ICIEA.2007.4318571   Google Scholar

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.
DOI: https://doi.org/10.1049/cim2.12043   Google Scholar

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Published
2022-09-30

Cited by

Palchevskyi, B., & Krestyanpol, L. (2022). APPLICATION OF PREDICTIVE MAINTENANCE IN THE PACKAGING PRODUCTION. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(3), 27–33. https://doi.org/10.35784/iapgos.3051

Authors

Bogdan Palchevskyi 
bogdan_pal@ukr.net
Lutsk National Technical University Ukraine
http://orcid.org/0000-0002-4000-4992

Authors

Lyubov Krestyanpol 

Lesya Ukrainka Eastern European National University Ukraine
http://orcid.org/0000-0003-3617-7900

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