A MODEL FOR ASSESSING THE LEVEL OF AUTOMATION OF A MAINTENANCE DEPARTMENT USING ARTIFICIAL NEURAL NETWORK
Daniel HALIKOWSKI
University of Applied Science in Nysa, Institute of Technical Science, ul. Armii Krajowej 7, 48-300 Nysa (Poland)
Justyna PATALAS-MALISZEWSKA
University of Zielona Góra, Faculty of Mechanical Engineering, Institute of Computer Science and Production Management, Licealna 9 Street, 65-417 Zielona Góra (Poland)
Małgorzata SKRZESZEWSKA
* University of Zielona Góra, Faculty of Mechanical Engineering, Institute of Computer Science and Production Management, Licealna 9 Street, 65-417 Zielona Góra (Poland)
Abstract
With regard to adapting enterprise to the Industry 4.0 concept, the first element should be the implementation and use of an information system within a manufacturing company. This article proposes a model, the use of which will allow the level of automation of a maintenance department to be forecast, depending on the effectivity of the use of the Manufacturing Executions System (MES) within a company. The model was built on the basis of the actual times of business processes completed which were supported by MES in the maintenance departments of two manufacturing enterprises using artificial neural network. As a result of research experiments, it was confirmed that the longer the time taken to complete business processes supported by MES, the higher is the degree of automation in a maintenance department.
Keywords:
Maintenance department, Artificial neural network, Manufacturing companiesReferences
Bojar, W., & Żółtowski, M. (2011). Procesy wspomagania decyzji w zakresie utrzymania ruchu i eksploatacji maszyn. Studia i Materialy Polskiego Stowarzyszenia Zarzadzania Wiedza, 40, 71–84.
Google Scholar
Gawlik, J., & Kiełbus, A. (2012). Zastosowania metod sztucznej inteligencji w nadzorowaniu urządzeń technologicznych i jakości wyrobów. In T. Sikora & M. Giemza (Eds.), Praktyka zarządzania jakością w XXI wieku (pp. 508-534). Kraków, Poland: Wydawnictwo Naukowe PTTŻ.
Google Scholar
Huda, A. N., & Taib, S. (2013). Application of infrared thermography for predictive/preventive maintenance of thermal defect in electrical equipment. Applied Thermal Engineering, 61(2), 220–227. https://doi.org/10.1016/j.applthermaleng.2013.07.028
DOI: https://doi.org/10.1016/j.applthermaleng.2013.07.028
Google Scholar
Jacobson, S., Masson, C., Smith, A. & Souza, J. (2005). AMR Research Report 18059, MES Market Rides Perfect Storm Through $1 B Barrier. AMR Research, 2–18.
Google Scholar
Jacobson, S. & Masson, C. (2006). Eyelit: MES Lite: Building MES Composite Applications With Operations Process Management. Retrieved from http://eyelit.com/simon.html.
Google Scholar
Kosicka, E., Mazurkiewicz, D., & Gola, A. (2016). Problemy wspomagania decyzji w systemach utrzymania ruchu. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 4, 49–52. https://doi.org/10.5604/01.3001.0009.5189
DOI: https://doi.org/10.5604/01.3001.0009.5189
Google Scholar
Li, Z., Wang, Y., & Wang, K. S. (2017). Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Advances in Manufacturing, 5(4), 377–387.
Google Scholar
https://doi.org/10.1007/s40436-017-0203-8
DOI: https://doi.org/10.1007/s40436-017-0203-8
Google Scholar
Lipski J., & Pizoń J. (2014), Sztuczna inteligencja w inżynierii produkcji. In J. Lipski, A. Świć, & A. Bojanowska (Eds.), Innowacyjne metody w inżynierii produkcji (pp. 11–24). Lublin, Poland: Wydawnictwo Politechniki Lubelskiej.
Google Scholar
Raptodimos, Y., & Lazakis, I. (2016). An artificial neural network approach for predicting the performance of ship machinery equipment. In Maritime Safety and Operations 2016 Conference Proceedings (pp. 95–101). Glasgow, UK: University of Strathclyde Publishing.
Google Scholar
Seitz K.-F. & Nyhuis P. (2015). Cyper-Physical Production Systems Combined with Logistic Models – A Learning Factory Concept for an Improved Production Planning and Control. CIRP Procedia, 32, 92–97. https://doi.org/10.1016/j.procir.2015.02.220
DOI: https://doi.org/10.1016/j.procir.2015.02.220
Google Scholar
Wu, B., Tian, Z., & Chen, M. (2013). Condition‐based maintenance optimization using neural network‐based health condition prediction. Quality and Reliability Engineering International, 29(8), 1151–1163. https://doi.org/10.1002/qre.1466
DOI: https://doi.org/10.1002/qre.1466
Google Scholar
Authors
Daniel HALIKOWSKIUniversity of Applied Science in Nysa, Institute of Technical Science, ul. Armii Krajowej 7, 48-300 Nysa Poland
Authors
Justyna PATALAS-MALISZEWSKAUniversity of Zielona Góra, Faculty of Mechanical Engineering, Institute of Computer Science and Production Management, Licealna 9 Street, 65-417 Zielona Góra Poland
Authors
Małgorzata SKRZESZEWSKA* University of Zielona Góra, Faculty of Mechanical Engineering, Institute of Computer Science and Production Management, Licealna 9 Street, 65-417 Zielona Góra Poland
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