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Hybrid predictive maintenance model – study and implementation example

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Języki publikacji
EN
Abstrakty
EN
In this paper, the concept of hybrid predictive maintenance for a single industrial machine is presented. A review of the solutions in the area of machine maintenance (especially predictive maintenance) which have been described in the literature is provided. The assumptions of the hybrid predictive maintenance model for modules, machines, or systems are presented. The methods used within the developed methodology are described. This includes the use of diagnostic data, experience, and a mathematical model. A case study of an industrial machine on which a system for collecting diag nostic data has been pilot-implemented, using, among others, vibration sensors and drive system pa rameters for damage detection is presented. The registered data can be used to precisely determine the time of upcoming failure after detection of the characteristic symptoms resulting from component wear In addition, an analysis of the durations of correct operation and failure events was performed and indicators describing these values were determined. The values of the aforementioned indicators were determined based on empirical data and described using a gamma distribution. The objective of the research was to prepare, implement and draw conclusions on a hybrid predictive maintenance model. A real industrial machine was used in the research study. The hybrid predictive maintenance model presented in this paper enables the use of data of different types (diagnostic, historical and mathemat ical model-based) in scheduling machine downtime for maintenance actions. On the basis of the re search conducted, it was determined which machine operating parameters are characterised by varia bility that enables the detection of upcoming failure. This allows for precise planning of maintenance activities and minimization of unplanned downtime.
Rocznik
Strony
285--295
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
  • 1. Achouch,M., Dimitrova,M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., Adda, M., 2022. On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences, 12, 8081, DOI: 10.3390/app12168081
  • 2. Ahmed, U., Carpitella, S., Certa, A., 2021. An integrated methodological ap proach for optimising complex systems subjected to predictive mainte nance. Reliability Engineering & System Safety, 216, 108022, DOI: 10.1016/j.ress.2021.108022
  • 3. Cao, Q., Zanni-Merk, C., Samet, A., Reich, C., Beuvron, F., Beckmann, A., Giannetti, C., 2022. KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0. Robotics and Computer-Integrated Manu facturing, 74, 102281, DOI: 10.1016/j.rcim.2021.102281
  • 4. Carnero, M.C., Gomez, A., 2017. Maintenance strategy selection in electric power distribution systems. Energy, Volume 129, 255-272, DOI: 10.1016/j.energy.2017.04.100
  • 5. Daniewski, K., Kosicka, E., Mazurkiewicz, D, 2018. Analysis of the correct ness of determination of the effectiveness of maintenance service actions. Management and Production Engineering Review, 9(2), 20-25, DOI: 10.24425/119522
  • 6. Fossier, S., Robic, P.O., 2017. Maintenance of Complex Systems – From Pre ventive to Predictive. 12th International Conference on Live Maintenance (ICOLIM), IEEE, 1-6.
  • 7. Ighravwe, D.E, Oke, S.A., 2019. A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria. Journal of Building Engineering, 24, 100753, DOI: 10.1016/j.jobe.2019.100753
  • 8. Ji, B., Bang, S., Park, H., Cho, H., 2019. Multi-Criteria Decision-Making Based Critical Component Identification and Prioritization for Predictive Maintenance. Industrial Engineering & Management Systems 18(3), 305–314, DOI: 10.7232/iems.2019.18.3.305
  • 9. Keleko, A.T., Kamsu-Foguem, B., Ngouna, R.H., Tongne, A., 2022. Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibli ometric analysis. AI and Ethics 2, 553–577, DOI: 10.1007/s43681-021 00132-6
  • 10. Kumar, A.S., Iyer, E., 2019. An industrial IoT engineering and manufacturing industries – benefits and challenges. International Journal of Mechanical and Production Engineering Research and Development (IJMPERD), 9(2), 151-160, DOI: 10.24247/ijmperdapr201914
  • 11. Lampropoulos, G., Siakas, K., Anastasiadis, T., 2018. Internet of Things (IoT) in Industry: Contemporary Application Domains, Innovative Technolo gies and Intelligent Manufacturing. International Journal of Advances in Scientific Research and Engineering (ijasre), 4(10), 109-118, DOI: 10.31695/IJASRE.2018.32910
  • 12. Lisnianski, A., Frenkel, I., Khvatskin, L., 2021. Modern Dynamic Reliability Analysis for Multi-State Systems. Springer: Berlin/Heidelberg, Ger many, DOI: 10.1007/978-3-030-52488-3
  • 13. Luo, W., Hu, T., Ye, Y., Zhang, C., Wei, Y., 2020. A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin. Ro botics and Computer-Integrated Manufacturing, 65, 101974, DOI: 10.1016/j.rcim.2020.101974
  • 14. Mallioris, P., Aivazidou, E., Bechtsis, D., 2024. Predictive maintenance in Industry 4.0: A systematic multi-sector mapping. CIRP Journal of Man ufacturing Science and DOI: 10.1016/j.cirpj.2024.02.003
  • 15. Moleda, M., Malysiak-Mrozek, B., Ding, W., Sunderam, V., Mrozek, D., 2023. From Corrective to Predictive Maintenance—A Review of Mainte nance Approaches for the Power Industry. Sensors, 23(13), 5970, DOI: 10.3390/s23135970
  • 16. Nunes, P., Santos, J., Rocha, E., 2023. Challenges in predictive maintenance – A review. CIRP Journal of Manufacturing Science and Technology, Volume 40, 53-67, DOI: 10.1016/j.cirpj.2022.11.004
  • 17. Randall, R.B., 2011. Vibration-based condition monitoring: industrial, aero space and automotive applications. John Wiley & Sons Ltd, New York, USA, DOI: 10.1002/9780470977668
  • 18. Rosati, R., Romeo, L., Cecchini, G., Tonetto, F., Viti, P., Mancini, A., Fron toni, E., 2022. From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0. Journal of Intelligent Manufacturing 34, 107–121, DOI: 10.1007/s10845-022-01960-x
  • 19. Scope, C., Vogel, M., Guenther, E., 2021.Greener, cheaper, or more sustain able: Reviewing sustainability assessments of maintenance strategies of concrete structures. Sustainable Production and Consumption, 26, 838 858, DOI: 10.1016/j.spc.2020.12.022
  • 20. Shafiee, M., Labib, A., Maiti, J., Starr, A., 2019. Maintenance strategy selec tion for multi-component systems using a combined analytic network process and cost-risk criticality model. Journal of Risk and Reliability, Proc IMechE Part O: J Risk and Reliability, 1–16, DOI: 10.1177/1748006X17712071
  • 21. Stodola, P., Stodola, J., 2020. Model of Predictive Maintenance of Machines and Equipment. Applied Sciences, 10, 213, DOI: 10.3390/app10010213
  • 22. Tiddens,W., Braaksma, J., Tinga, T., 2023. Decision Framework for Predic tive Maintenance Method Selection. Applied Sciences, 13, 2021, DOI: 10.3390/app13032021
  • 23. Tran, M., Elsisi, M., Mahmoud, K., Liu, M., Lehtonen, M., Darwish, M. M. F., 2021. Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment. IEEE Access, 9, 115429-115441, DOI: 10.1109/ACCESS.2021.3105297
  • 24. Wiercioch, J., 2023. Development of a hybrid predictive maintenance model. Journal of KONBiN, 53(2), 141-158, DOI: 10.5604/01.3001.0053.7130
  • 25. Yazdi, M., 2024. Maintenance Strategies and Optimization Techniques. In: Advances in Computational Methematics for Industrial System Reliabil ity and Maintainability. Springer Series in Reliability Engineering, Springer Cham, 59-77, DOI: 10.1007/978-3-031-53514-7_4
  • 26. Zhang, M., Amaitik, N., Wang, Z., Xu, Y., Maisuradze, A., Peschl, M., Tzovaras, D., 2022. Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction. Applied Sciences, 12, 3218, DOI: 10.3390/app12073218
  • 27. Zhao, J., Gao, C., Tang, T., 2022. A Review of Sustainable Maintenance Strat egies for Single Component and Multicomponent Equipment. Sustaina bility, 14, 2992, DOI: 10.3390/su14052992
  • 28. Zwolińska, B., Wiercioch, J., 2022. Selection of Maintenance Strategies for Machines in a Series-Parallel System. Sustainability, 14, 11953, DOI: 10.3390/su141911953
  • 29. Zwolińska, B., 2019. Modeling convergent processes in complex production systems. Wydawnictwa AGH, Krakow, Poland
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e1714f3a-635c-4686-9e6b-39c63b70e9ec
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