ZASTOSOWANIE PREDYKCYJNEJ DIAGNOSTYKI W PRODUKCJI OPAKOWAŃ

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