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Predictive management in the context of industry 4.0

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents information on the potential of Industry 4.0 in the field of maintenance. This work explores the potential and trends of predictive maintenance management in an industrial big data environment. The development of predictive maintenance, its technical challenges and in the context of Industry 4.0 was presented. In addition, a case study that illustrates how maintenance management and predictive maintenance can be applied to the maintenance of wind turbines is discussed.
Wydawca
Rocznik
Strony
41--52
Opis fizyczny
Bibliogr. 33 poz., tab.
Twórcy
autor
  • Czestochowa University of Technology, Poland
Bibliografia
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  • 2.Aljumaili, M., Wandt, K., Karim, R., Tretten, P., 2015. eMaintenance ontologies for data quality support. Journal of Quality in Maintenance Engineering, 21(3), 358-374. DOI: 10.1108/JQME-09-2014-0048.
  • 3.Ben Ali, J., Fnaiech, N., Saidi, L., Chebel-Morello, B., Fnaiech, F., 2015. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16-27. DOI: 10.1016/j.apacoust.2014.08.016.
  • 4.Boyd, S., 2010. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations and Trends® in Machine Learning, 3(1), 1-122. DOI: 10.1561/2200000016.
  • 5.Chen, J., Zhang, R., Di Wu., 2018. Equipment Maintenance Business Model Innovation for Sustainable Competitive Advantage in the Digitalization Context: Connotation, Types, and Measuring. Sustainability, 10(11), 3970. DOI: 10.3390/su10113970.
  • 6.Cheng, S., Pecht, M., 2007. Multivariate state estimation technique for remaining useful life prediction of electronic products. Downloaded from: https://www.aaai.org/papers/symposia/fall/2007/fs-07-02/fs07-02-004.pdf.
  • 7.Ding, S.X., 2008. Model-based fault diagnosis techniques: Design schemes, algorithms, and tools. New York: Springer.
  • 8.Doebling, S.W., Farrar, C.R., Prime, M.B., 1998. A Summary Review of VibrationBased Damage Identification Methods. The Shock and Vibration Digest, 30(2), 91-105. DOI: 10.1177/058310249803000201.
  • 9.Finkenzeller, K., 2010. RFID handbook: Fundamentals and applications in contactless smart cards, radio frequency identification and near-field communication (3rd edition). [Hoboken (N.J.)]: Wiley; IEEE Press.
  • 10.Gaushell, D.J., Darlington, H.T., 1987. Supervisory control and data acquisition. Proceedings of the IEEE, 75(12), 1645-1658. DOI: 10.1109/PROC.1987.13932.
  • 11.Ingaldi, M., Ulewicz, R., 2020. Problems with the Implementation of Industry 4.0 in Enterprises from the SME Sector. Sustainability, 12(1), 217. DOI: 10.3390/su12010217.
  • 12.Jardine, A.K., Lin, D., Banjevic, D., 2006. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. DOI: 10.1016/j.ymssp.2005.09.012.
  • 13.Jasiulewicz-Kaczmarek, M., Legutko, S., Kluk, P., 2020. Maintenance 4.0 technologies – new opportunities for sustainability driven maintenance. Management and Production Engineering Review, 11(2), 74-87. DOI: 10.24425/MPER.2020.133730.
  • 14.Jimenez-Cortadi, A., Irigoien, I., Boto, F., Sierra, B., Rodriguez, G., 2020. Predictive Maintenance on the Machining Process and Machine Tool. Applied Sciences, 10(1), 224. DOI: 10.3390/app10010224.
  • 15.Knop, K., 2019. Analysis and Improvement of the Galvanized Wire Production Process with the use of DMAIC Cycle. Quality Production Improvement - QPI, 1(1), 551-558. DOI: 10.2478/cqpi-2019-0074.
  • 16. Knop, K., 2021. Management of Packaging Labeling Technology in the Context of Improving the Final Product Quality and Work Safety. System Safety: Human - Technical Facility-Environment, 3(1), 116-128. DOI: 10.2478/czoto-2021-0013.
  • 17.Krynke, M., Czaja, P., Zasadzień, M., 2016. Systemy techniczne: Technologia, jakość, eksploatacja. Częstochowa: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji.
  • 18.Krzanowski, W.J., 2000. Principles of multivariate analysis: A user’s perspective (Revised ed.). Oxford statistical science series: nr. 22. Oxford: Oxford University Press.
  • 19.Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M., 2014. Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242. DOI: 10.1007/s12599-014-0334- 4.
  • 20.Li, Z., Wang, K., He, Y., 2016. Industry 4.0 - Potentials for Predictive Maintenance. Atlantis Press. Downloaded from: https://ntnuopen.ntnu.no/ntnuxmlui/handle/11250/2599772.
  • 21.Mazur, M., 2019. Quality Assurance Processes in Series Production of Car Elements. Quality Production Improvement - QPI, 1(1), 610-617. DOI: 10.2478/cqpi-2019-0082.
  • 22.Mazur, M., Momeni, H., 2019. LEAN Production issues in the organization of the company - results. Production Engineering Archives, 22(22), 50-53. DOI: 10.30657/pea.2019.22.10.
  • 23.Mielczarek, K., Krynke, M., 2018. Plastic production machinery – the evaluation of effectiveness. Production Engineering Archives, 18(18), 42-45. DOI: 10.30657/pea.2018.18.07.
  • 24.Mohanty, S., Listwan, J., 2021. An Artificial Intelligence/Machine Learning Based Regression Clustering-Sensor-Fusion Framework for Predicting Unmeasurable Time-Series Strain From Other Sensor Measurements. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 5(1). DOI: 10.1115/1.4050592.
  • 25.Nowak, M., 2016. Zastosowanie szarego modelu GM (1,1) w predykcji krótkich szeregów finansowych na przykładzie przedsiębiorstw sektora produkcji materiałów budowlanych. Zeszyty Naukowe Politechniki Poznańskiej Organizacja i Zarządzanie, 155-164. DOI: 10.21008/j.0239 9415.2016.070.11.
  • 26.Pattanapairoj, S., Nitisiri, K., Sethanan, K., 2021. A Gap Study between Employers’ Expectations in Thailand and Current Competence of Master’s Degree Students in Industrial Engineering under Industry 4.0. Production Engineering Archives, 27(1), 50-57. DOI: 10.30657/pea.2021.27.7.
  • 27.Perzyńska, J., 2010. Budowa prognoz kombinowanych z wykorzystaniem sztucznych sieci neuronowych (nr. 103). Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu nr 103. Downloaded from: https://dbc.wroc.pl/content/74165/perzynska_budowa_prognoz_kombinowanych.pdf.
  • 28.Pietraszek, J., Radek, N., Goroshko, A.V., 2020. Challenges for the DOE methodology related to the introduction of Industry 4.0. Production Engineering Archives, 26(4), 190-194. DOI: 10.30657/pea.2020.26.33.
  • 29.Ślusarczyk, B., 2018. INDUSTRY 4.0 – ARE WE READY? Polish Journal of Management Studies, 17(1), 232-248. DOI: 10.17512/pjms.2018.17.1.19.
  • 30.Ulewicz, R., Mazur, M., 2019. Economic Aspects of Robotization of Production Processes by Example of a Car Semi-trailers Manufacturer. Manufacturing Technology, 19(6), 1054-1059. DOI: 10.21062/ujep/417.2019/a/1213- 2489/MT/19/6/1054.
  • 31.Wang, Z., Liu, C., 2021. Wind turbine condition monitoring based on a novel multivariate state estimation technique. Measurement, 168, 108388. DOI: 10.1016/j.measurement.2020.108388.
  • 32.Zasadzień, M., 2017. Six Sigma methodology as a road to intelligent maintenance. Production Engineering Archives, 15(15), 45-48. DOI: 10.30657/pea.2017.15.11.
  • 33.Derenda T., Zanne M., Zoldy M., Torok A., 2018. Automatization in road transport: a review. Production Engineering Archives vol. 20, pp 3-7.
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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2a0dfc3c-ab6e-451c-bcb5-392163dae21c
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