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Time-based machine failure prediction in multi-machine manufacturing systems

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Warianty tytułu
PL
Algorytm wsparcia strategii tbm w wielomaszynowych systemach wytwórczych
Języki publikacji
EN PL
Abstrakty
EN
The execution of production processes in real manufacturing systems is associated with the occurrence of numerous disruptions, which predominantly revolve around technological machine failure. Therefore, various maintenance strategies are being developed, many of which tend to emphasise effective preventive measures, such as the Time-Based Maintenance (TBM) discussed in this paper. Specifically, this publication presents the time-based machine failure prediction algorithm for the multi-machine manufacturing environment. The Introduction section outlines the body of knowledge related to typical strategies applied in maintenance. The next part describes an approach to failure prediction that treats processing times as makespan and is followed by highlighting the key role of historical data in machine failure management, in the subsequent section. Finally, the proposed time-based machine failure prediction algorithm is presented and tested by means of a two-step verification, which confirms its effectiveness and further practical implementation.
PL
Realizacja procesów produkcyjnych w rzeczywistych systemach wytwórczych wiąże się z występowaniem wielu zakłóceń, do których zalicza się głównie awarie maszyn technologicznych. W związku z tym obserwowany jest rozwój różnorodnych strategii utrzymania ruchu. Coraz większy nacisk kładziony jest na efektywne działania prewencyjne, do których zalicza się także działania określone w czasie (ang. Time-Based Maintenance – TBM). W niniejszej publikacji zaprezentowano algorytm predykcji awarii maszyn w wielomaszynowych systemach wytwórczych wspierający prewencyjne utrzymanie ruchu. Na wstępie omówiono zagadnienia związane z typowymi strategiami stosowanymi w obszarze UR. Ponadto omówiono tematykę predykcji awarii, zwracając uwagę na ujęcie czasu pracy maszyny jako czasu trwania, a także kluczową rolę wykorzystania danych historycznych dotyczących awarii maszyn. Następnie zaprezentowano proponowany algorytm predykcji wspierający działania określone w czasie. Prezentowane prace zakończono dwuetapową weryfikacją proponowanej metody, która potwierdziła jej skuteczność oraz zasadność wykorzystania.
Rocznik
Strony
52--62
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
  • Department of Production Computerisation and Robotisation Faculty of Mechanical Engineering, Lublin University of Technology ul. Nadbystrzycka 36, 20-816 Lublin, Poland
  • Department of Production Computerisation and Robotisation Faculty of Mechanical Engineering, Lublin University of Technology ul. Nadbystrzycka 36, 20-816 Lublin, Poland
  • Department of Production Computerisation and Robotisation Faculty of Mechanical Engineering, Lublin University of Technology ul. Nadbystrzycka 36, 20-816 Lublin, Poland
Bibliografia
  • 1. Albrice D, Branch M. A Deterioration Model for Establishing an Optimal Mix of Time-Based Maintenance (TbM) and Condition-Based Maintenance (CbM) for the Enclosure System. Fourth Building Enclosure Science & Technology Conference (BEST4), Kansas City, Missouri, April 13–15, 2015.
  • 2. Al-Hinai N, ElMekkawy TY. Robust and Stable Flexible Job Shop Scheduling with Random Machine Breakdowns Using a Hybrid Genetic Algorithm. International Journal of Production Economics 2011; 132(2): 279–291, http://dx.doi.org/10.1016/j.ijpe.2011.04.020.
  • 3. Antosz K, Stadnicka D. Evaluation measures of machine operation effectiveness in large enterprises: study results. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2015; 17(1): 107–117, http://dx.doi.org/10.17531/ein.2015.1.15.
  • 4. Baptista M, Sankararaman S, de Medeiros IP, Nascimento C, Prendinger H, Henriques EMP. Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling, Computers & Industrial Engineering 2018; 115: 41–53, https://doi. org/10.1016/j.cie.2017.10.033.
  • 5. Bartochowska D, Ferenc R. Instrumenty wsparcia utrzymania ruchu w małych i średnich przedsiębiorstwach. Zeszyty naukowe Politechniki Śląskiej 2015; 80: 21–50.
  • 6. Bei XQ, Zhu XY, Coit DW. A risk-averse stochastic program for integrated system design and preventive maintenance planning. European Journal Of Operational Research 2019; 276(2): 536–548, http://dx.doi.org/10.1016/j.ejor.2019.01.038.
  • 7. Bräsel H, Dornheim L, Kutz S, Mörig M, Rössling I. LiSA – A Library of Scheduling Algorithms. Magdeburg University, 2001.
  • 8. Davenport A, Gefflot C, Beck C. Slack-based Techniques for Robust Schedules. Sixth European Conference on Planning, Toledo, Spain, September 12–14, 2001.
  • 9. Deepu P. Robust Schedules and Disruption Management for Job Shops. Bozeman, Montana, 2008.
  • 10. Fernandes M, Canito A, Bolon-Canedo V, Conceicao L, Praca I, Marreiros G. Data analysis and feature selection for predictive maintenance:A case-study in the metallurgic industry. International Journal Of Information Management 2019, 45: 252–262, http://dx.doi.org/10.1016/j.ijinfomgt.2018.10.006.
  • 11. Frątczak E, Sienkiewicz U, Babiker H. Analiza historii zdarzeń – Elementy teorii, wybrane przykłady zastosowań. Oficyna Wydawnicza Szkoła Główna Handlowa w Warszawie, Warszawa 2014.
  • 12. Gao H. Bulding Robust Schedules using Temporal Potection – An Empirical Study of Constraint Based Scheduling Under Machine Failure Uncertainty. Toronto, Ontario, 1996.
  • 13. Gao Y, Feng Y, Zhang Z, Tan J. An optimal dynamic interval preventive maintenance scheduling for series systems. Reliability Engineering & System Safety 2015; 142: 19–30, http://dx.doi.org/10.1016/j.ress.2015.03.032.
  • 14. Gola A. Reliability analysis of reconfigurable manufacturing structures using computer simulation methods. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21(1): 90–102, http://dx.doi.org/10.17531/ein.2019.1.11.
  • 15. Gürel S, Körpeoḡlu E, Aktürk MS. An Anticipative Scheduling Approach with Controllable Processing Times. Computers & Operations Research 2010; 37(6): 1002–1013, http://dx.doi.org/10.1016/j.cor.2009.09.001.
  • 16. Jasiulewicz-Kaczmarek M, Bartkowiak T. Improving the performance of a filling line based on simulation, ModTech International Conference – Modern Technologies in Industrial Engineering IV, Romania, Iasi, June 15–18, IOP Conf. Series: Materials Science and Engineering 2016; 145(042024), https://doi.org/10.1088/1757-899X/145/4/042024.
  • 17. Jensen MT. Improving robustness and flexibility of tardiness and total flow-time job shops using robustness measures. Applied Soft Computing 2001; 1: 35–52, http://dx.doi.org/10.1016/S1568-4946(01)00005-9.
  • 18. Jian X, Li-Ning X, Ying-Wu Ch. Robust Scheduling for Multi-Objective Flexible Job-Shop Problems with Random Machine Breakdowns. International Journal of Production Economics 2013; 141(1): 112–126, https://doi.org/10.1016/j.ijpe.2012.04.015.
  • 19. Kalinowski K, Krenczyk D, Grabowik C. Predictive-reactive strategy for real time scheduling of manufacturing systems. Applied Mechanics and Materials 2013; 307: 470–473, https://doi.org/10.4028/www.scientific.net/AMM.307.470.
  • 20. Kempa W, Paprocka I, Kalinowski K, Grabowik C. Estimation of reliability characteristics in a production scheduling model with failures and time-changing parameters described by Gamma and exponential distributions. Advanced Materials Research 2014; 837: 116–121.
  • 21. Kempa W, Wosik I, Skołud B. Estimation of Reliability Characteristics in a Production Scheduling Model with Time-Changing Parameters – First Part, Theory. Management and Control of Manufacturing Processes. Lublin, 2011; 7–18.
  • 22. Kłos S, Patalas-Maliszewska J, Trebuna P. Improving manufacturing processes using simulation methods. Applied Computer Science 2016; 12(4): 7–17.
  • 23. Lawless J. F. Statistical Models and Methods for Lifetime Data. John Wiley & Sons, 2003.
  • 24. Leon VJ., Wu SD., Storer RH. Robustness Measures and Robust Scheduling for Job Shops. IIE transactions 1994; 26(5): 32–43, https://doi.org/10.1080/07408179408966626.
  • 25. Liao W, Zhang X, Jiang M. An optimization model integrated production scheduling and preventive maintenance for group production. IEEE International Conference on Industrial Engineering and Engineering Management 2016; December, 936–940, http://dx.doi.org/10.1109/IEEM.2016.7798015.
  • 26. Loska A. Scenario modeling exploitation decision-making process in technical network systems. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2017; 19 (2): 268–278, http://dx.doi.org/10.17531/ein.2017.2.15.
  • 27. Lü Y, Zhang Y. Reliability Modeling and Maintenance Policy Optimization for Deteriorating System Under Random Shock. Journal of Shanghai Jiaotong University (Science) 2018; 23(6): 791–797, http://dx.doi.org/10.1007/s12204-018-1985-y.
  • 28. Mehta SV., Uzsoy RM. Predictable Scheduling of a Job Shop Subject to Breakdowns. IEEE Transactions on Robotics and Automation 1998; 14(3): 365–378, https://doi.org/10.1109/70.678447.
  • 29. Rawat M, Lad BK., Novel approach for machine tool maintenance modelling and optimization using fleet system architecture. Computers & Industrial Engineering 2018; 126: 47–62, http://dx.doi.org/10.1016/j.cie.2018.09.006.
  • 30. Rosmaini A, Shahrul K. An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering 2012; 63(1): 135–149, http://dx.doi.org/10.1016/j.cie.2012.02.002.
  • 31. Sabuncuoglu I, Bayõz M. Analysis of reactive scheduling problems in a job shop environment. European Journal of Operational Research 2000; 126(3): 567–586, https://doi.org/10.1016/S0377-2217(99)00311-2.
  • 32. Skołud B., Wosik I., Immune Algorithms in Production Jobs Scheduling. Zarządzanie Przedsiębiorstwem 2008; 1: 47–48.
  • 33. Sobaszek Ł, Gola A, Kozłowski E. Job-shop scheduling with machine breakdown prediction under completion time constraint. Annals of Computer Science and Information Systems 2018; 15: 437–440, http://dx.doi.org/10.15439/2018F83.
  • 34. Szwedzka K, Szafer P, Wyczółkowski R. Structural analysis of factors affecting the effectiveness of complex technical systems. Proceedings of the 30th International Business Information Management Association Conference, IBIMA 2017 – Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth Volume 2017, 4096–4105.
  • 35. Timofiejczuk A, Brodny J, Loska A. Exploitation Policy in the Aspect of Industry 4.0 Concept – Overview of Selected Research. Multidisciplinary Aspects of Production Engineering 2018; 1(1): 353–359, https://doi.org/10.2478/mape-2018-0045.
  • 36. Vonta F. Frailty or Transformation Models in Survival Analysis and Reliability. Recent Advances In System Reliability: Signatures, Multi-State Systems And Statistical Inference 2012; 237–251, http://dx.doi.org/10.1007/978-1-4471-2207-4_17.
  • 37. Wei-Wei C, Zhiqiang L, Ershun P. Integrated Production Scheduling and Maintenance Policy for Robustness in a Single Machine. Computers & Operations Research 2014; 47: 81–91, https://doi.org/10.1016/j.cor.2014.02.006.
  • 38. Yang BY, Liu RN, Zio E. Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture. IEEE Transactions On Industrial Electronics 2019; 66(12): 9521–9530, https://doi.org/10.1109/TIE.2019.2924605.
  • 39. Zhang F, Shen J, Ma Y. Optimal maintenance policy considering imperfect repairs and non-constant probabilities of inspection errors. Reliability Engineering and System Safety 2020; 193: 1–12, http://dx.doi.org/10.1016/j.ress.2019.106615.
  • 40. Zhao X, He S, He Z, Xie M. Optimal condition-based maintenance policy with delay for systems subject to competing failures under continuous monitoring. Computers & Industrial Engineering 2018; 124: 535–544, http://dx.doi.org/10.1016/j.cie.2018.08.006.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9c26cb60-5d1f-46b0-b7c9-1d59c7a3c228
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