Development of a hybrid predictive maintenance model

Wiercioch, Jakub

doi:10.5604/01.3001.0053.7130

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Artykuł - szczegóły

Tytuł artykułu

Development of a hybrid predictive maintenance model

Autorzy

Wiercioch Jakub

Wybrane pełne teksty z tego czasopisma

https://journalofkonbin.com

Identyfikatory

DOI

10.5604/01.3001.0053.7130

Warianty tytułu

Opracowanie hybrydowego modelu predykcyjnego utrzymania ruchu

Języki publikacji

Abstrakty

Progress in the field of technology and science enables the digitalization of manufacturing processes in the era of Industry 4.0. For this purpose, it uses tools which are referred to as the technological pillars of Industry 4.0. Simultaneously with the changes in the field of manufacturing, the interdisciplinary cooperation between production and machine maintenance planning is developing. Different types of predictive maintenance models are being developed in order to ensure the good condition of the machines, optimize maintenance costs and minimize machine downtime. The article presents the existing types of predictive maintenance and selected methods of machine diagnostics that can be used to analyze machines operating parameters. A hybrid model of predictive maintenance was developed and described. The proposed model is based on diagnostic data, historical data on failures and mathematical models. The use of complementary types of predictive maintenance in the hybrid model of predictive maintenance is particularly important in the case of high-performance production lines, where high quality of products and timeliness of orders are crucial.

Postęp w dziedzinie techniki i nauki umożliwia digitalizację procesów wytwórczych w erze Przemysłu 4.0. Wykorzystuje w tym celu narzędzia, które określane są jako filary technologiczne Przemysłu 4.0. Równocześnie ze zmianami w dziedzinie produkcji rozwija się interdyscyplinarna współpraca między produkcją a planowaniem obsługi maszyn. W celu utrzymania maszyn w należytej kondycji oraz optymalizacji kosztów obsługi i czasów przestojów, rozwijają się różne typy predykcyjnych modeli obsługi. W artykule przedstawione zostały istniejące typy predykcyjnej obsługi oraz wybrane metody diagnostyki maszyn, które mogą zostać wykorzystane do badania parametrów pracy maszyn. Opracowany oraz opisany został hybrydowy model predykcyjnej obsługi, wykorzystujący dane diagnostyczne, dane historyczne dotyczące awarii oraz modele matematyczne. Wykorzystanie w hybrydowym modelu predykcyjnej obsługi uzupełniających się typów predykcyjnej obsługi jest szczególnie istotne w przypadku wysokowydajnych linii produkcyjnych, gdzie kluczowe są wysoka jakość wyrobów oraz terminowość wykonywanych zleceń.

Słowa kluczowe

maintenance predictive maintenance maintenance model diagnostics diagnostic method Industry 4.0 Maintenance 4.0 digitalisation

obsługa maszyn utrzymanie ruchu predykcyjna obsługa maszyn predykcyjne utrzymanie ruchu model utrzymania ruchu diagnostyka metoda diagnostyczna Przemysł 4.0 Utrzymanie ruchu 4.0 digitalizacja

Wydawca

Wydawnictwo Instytutu Technicznego Wojsk Lotniczych

Czasopismo

Journal of KONBiN

Rocznik

2023

Tom

Vol. 53, iss. 2

Strony

141--157

Opis fizyczny

Bibliogr. 50 poz., rys., tab.

Twórcy

autor

Wiercioch Jakub

AGH University of Science and Technology (Akademia Górniczo-Hutnicza)

https://orcid.org/0000-0002-9690-1094

Bibliografia

1. E. Michlowicz, “Logistics engineering and Industry 4.0 and digital factory”. Archives of Transport, Vol. 57, Iss. 1, 2021. DOI: 10.5604/01.3001.0014.7484.
2. Q. Cao, C. Zanni-Merk, A. Samet, C Reich., F.B. Beuvron, A. Beckmann and C. Gianetti, “KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0”. Robotics and Computer-Integrated Manufacturing, Vol. 74, 2022. DOI 10.1016/j.rcim.2021.102281.
3. L. Silvestri, A. Forcina, V. Introna and A. Santolamazza, “Maintenance transformation through Industry 4.0 technologies: A systematic literature review”. Computers in Industry, Vol. 123, 2020. Available: https://www.sciencedirect.com/science/article/pii/S0166361520305698?via%3Dihub
4. Y. Wen, M. F. Rahman, H. Xu and T.L.B. Tseng, “Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective”. Measurement, Vol. 187, 2022. Available: https://www.sciencedirect.com/science/article/pii/S0263224121011805
5. G. Erboz, “How to Define Industry 4.0: The Main Pillars Of Industry 4.0. Managerial trends in the development of enterprises in globalization era”, presented at Conference: Managerial trends in the development of enterprises in globalization era. Slovak University of Agriculture in Nitra, Slovakia, 2017.
6. E.O.B. Nara, M.B. Costa, I.C. Baierle, J.L. Schaefer, G.B. Benitez, L.M.A.L. Santos and L.B. Benitez, “Expected impact of industry 4.0 technologies on sustainable development: A study in the context of Brazil’s plastic industry”. Sustainable Production and Consumption, Vol. 25, 2021. Available: https://www.sciencedirect.com/science/article/pii/S2352550920305212?via%3Dihub
7. H. Boyes, B. Hallaq, J. Cunningham and T. Watson, “The industrial internet of things (IIoT): An analysis framework”. Computers in Industry, Vol. 101, 2018. Available: https://1library.net/document/y4we065q-industrial-internet-things-iiot-analysis-framework.html.
8. S. Jiang, Cao J., H. Wu and Y. Yang, “Fairness-based Packing of Industrial IoT Data in Permissioned Blockchains”. IEEE Transactions on Industrial Informatics, Vol. 17, Iss. 11, 2021. DOI 10.1109/TII.2020.3046129.
9. G. Aceto, V. Persico and A. Pescape, “Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0”. Journal of Industrial Information Integration, Vol. 18, 2020. Available: https://www.sciencedirect.com/science/article/pii/S2452414X19300135.
10. N.Z.N. Hasnan and Y.M. Yusoff, “Short review: Application Areas of Industry 4.0 Technologies in Food Processing Sector. In Proceedings of 16th Conference on Research and Development, Bangi, 2018. DOI 10.1109/SCORED.2018.8711184.
11. B. Motyl and S. Filippi, “Trends in engineering education for additive manufacturing in the industry 4.0 era: a systematic literature review”. International Journal on Interactive Design and Manufacturing, Vol. 15, 2021. Available: https://link.springer.com/article/10.1007/s12008-020-00733-1.
12. V. Madhavadas, D. Srivastava, U. Chadha, S.A. Raj, M.T.H. Sultan, F.S. Shahar and A.U.M. Shah, “A review on metal additive manufacturing for intricately shaped aerospace components”. CIRP Journal of Manufacturing Science and Technology, Vol. 39, 2022. Available: https://www.sciencedirect.com/science/article/pii/S1755581722001304.
13. A.T. Keleko, B. Kamsu-Foguem, R.H. Ngouna and A. Tongne, “Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis”. AI and Ethics, 2022. Available: https://link.springer.com/content/pdf/10.1007/s43681-021-00132-6.pdf.
14. M. Achouch, M. Dimitrova, K. Ziane, S. Sattarpanah Karganroudi, R. Dhouib, H. Ibrahim, M. Adda, “On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges”. Applied Sciences, Vol. 12, Iss. 16, 2022. Available: https://www.mdpi.com/2076-3417/12/16/8081.
15. Lamban M.P., Morella P., Royo J., Sanchez J.C.: Using industry 4.0 to face the challenges of predictive maintenance: A key performance indicators development in a cyber physical system. Computers and Industrial Engineering, Vol. 171, 2022. Available: https://www.sciencedirect.com/science/article/pii/S0360835222004417?via%3Dihub
16. A. Turygin, A. Mosyurchak, M. Zhalo and M. Hammer, “Maintenance and technical diagnostics of machine tools”. MM Science Journal, 2016. DOI 10.17973/MMSJ.2016_11_2016167.
17. H. Webert, T. Döß, L. Kaupp and S. Simons, “Fault Handling in Industry 4.0: Definition, Process and Applications”. Sensors, Vol. 22, Iss. 6, 2022. Available: https://www.mdpi.com/1424-8220/22/6/2205.
18. B. Zwolinska and J. Wiercioch, “Selection of Maintenance Strategies for Machines in a Series-Parallel System”. Sustainability, Vol. 14, Iss. 19, 2022. Available: https://www.mdpi.com/2071-1050/14/19/11953.
19. J.S. Kim, K.N. Choi and S.W. Kang, “Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities”. Sustainability, Vol. 13, Iss. 2, 2021. Available: https://www.mdpi.com/2071-1050/13/2/557.
20. A. Jablonski, “Condition Monitoring Algorithms in MATLAB”. Springer, 2021.
21. P. Poor, D. Zenisek and J. Basl, “Historical Overview of Maintenance Management Strategies: Development from Breakdown Maintenance to Predictive Maintenance in Accordance with Four Industrial Revolutions”. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Pilsen, 2019.
22. E. Garcia, N. Montés, J. Llopis and A. Lacasa, “Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era”. Sensors, Vol. 22, Iss. 16, 2022. Available: https://www.mdpi.com/1424-8220/22/16/6222.
23. B. Jonge, R. Teunter, T. Tinga, “The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance”. Reliability Engineering and System Safety, Vol. 158. 2017. Available: https://www.sciencedirect.com/science/article/pii/S0951832016306238?via%3Dihub.
24. G.K. Balakrishnan, C.T. Yaw, S.P. Koh, T. Abedin, A.A. Raj, S.K. Tiong and C.P. Chen, “A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations”. Energies, Vol. 15, Iss. 16, 2022. Available: https://www.mdpi.com/1996-1073/15/16/6000.
25. S. Arena, E. Florian, I. Zennaro, P.F. Orru and F. Sgarbossa, “A novel decision support system for managing predictive maintenance strategies based on machine learning approaches”. Safety Science, Vol. 146, 2022. Available: https://www.sciencedirect.com/science/article/pii/S0925753521003726.
26. T. Zonta, A.C. Costa, R.R. Righi, M.J. Lima, E.S. Trindade and G.P. Li, “Predictive Maintenance in the Industry 4.0: A systematic literature review”. Computers and Industrial Engineering, Vol. 150, 2020. Available: https://www.sciencedirect.com/science/article/pii/S0360835220305787?via%3Dihub.
27. W.C. Satyro, J.C. Contador, S.F.d.P. Monken et al., “Industry 4.0 Implementation Projects: The Cleaner Production Strategy — A Literature Review”. Sustainability, Vol. 15, Iss. 3, 2023. Available: https://www.mdpi.com/2071-1050/15/3/2161.
28. J. Li, D. Schaefer and J. Milisavljevic-Syed, “A Decision-Based Framework for Predictive Maintenance Technique Selection in Industry 4.0”. Proceedings of the 55th CIRP Conference on Manufacturing Systems, 2022. Available: https://www.sciencedirect.com/science/article/pii/S2212827122002293.
29. F. Arena, M. Collotta, L. Luca, M. Ruggieri and F.G. Termine, “Predictive Maintenance in the Automotive Sector: A Literature Review”. Mathematical and Computational Applications, Vol. 27, Iss. 1, 2022. Available: https://www.mdpi.com/2297-8747/27/1/2.
30. W. Tiddens, J. Braaksma, T. Tinga, “Decision Framework for Predictive Maintenance Method Selection”. Applied Sciences, Vol. 13, Iss. 3, 2023. Available: https://www.mdpi.com/2076-3417/13/3/2021.
31. H. Meriem, H. Nora, O. Samir, “Predictive Maintenance for Smart Industrial Systems: A Roadmap”. The 6th International Conference on Emerging Data and Industry (EDI40), Leuven, 2023.
32. J. Gama, R.P. Ribeiro, B. Veloso, Data-Driven Predictive Maintenance. IEEE Intelligent Systems, Vol. 37, No. 4, 2022. DOI 10.1109/MIS.2022.3167561.
33. J.J.M. Jimenez, S. Schwartz, R. Vingerhoeds, B. Grabot, M. Salaun, “Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics”. Journal of Manufacturing Systems, Vol. 56, 2020. Available: https://www.sciencedirect.com/science/article/pii/S0278612520301187.
34. P. Aivaliotis, Z. Arkouli, K. Georgoulias, S. Makris, “Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots”, Robotics and Computer-Integrated Manufacturing, Vol. 71, 2021. Available: https://www.sciencedirect.com/science/article/pii/S0736584521000600.
35. M. Uppal, D. Gupta, N. Goyal et al., “A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things”. Complexity, 2023. Available: https://www.hindawi.com/journals/complexity/2023/9991029.
36. J. Lee, M. Mitici, H. Blom, P. Bieber and F., “Freemen Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification”. Aerospace, Vol. 10, Iss. 2, 2023. Available: https://www.mdpi.com/2226-4310/10/2/186.
37. E.C. Ozkat, O. Bektas, M.J. Nielsen and A. Cour-Harbo, “A data-driven predictive maintenance model to estimate RUL in a multi-rotor UAS”. International Journal of Micro Air Vehicles, 2023. Available: https://journals.sagepub.com/doi/10.1177/17568293221150171.
38. M. Mitici, I. Pater, A. Barros and Z. Zeng, “Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines”. Reliability Engineering and System Safety, Vol. 234, 2023. Available: https://www.sciencedirect.com/science/article/pii/S095183202300114X.
39. E.G. Popkova, Y.V. Ragulina, A.V. Bogoviz, “Fundamental Differences of Transition to Industry 4.0 from Previous Industrial Revolutions”. In: Industry 4.0: Industrial Revolution of the 21st Century, Springer, 2019.
40. T. Mezair, Y. Djenouri, A. Belhadi, G. Srivastava and J.C. Lin, “A sustainable deep learning framework for fault detection in 6G Industry 4.0 heterogeneous data environments”. Computer Communications, Vol. 187, 2022. Available: https://www.sciencedirect.com/science/article/pii/S014036642200055X?via%3Dihub
41. C. Sassanelli, T. Arriga, S. Zanin, I. D’Adamo and S. Terzi, “Industry 4.0 Driven Result-oriented PSS: An Assessment in the Energy Management”. International Journal of Energy Economics and Policy, Vol. 12(4), 2022. Available: https://econjournals.com/index.php/ijeep/article/view/13313/6834.
42. K. Classens, S. Verbeek, M. Heemels and T. Oomen, “Joint Estimation of Additive and parametric Faults: A Model-Based Fault Diagnosis Approach towards Predictive Maintenance”. IFAC PapersOnLine, Vol. 55, Iss. 6, 2022. Available: https://www.sciencedirect.com/science/article/pii/S2405896322005316?via%3Dihub.
43. M. Zhang, N. Amaitik, Z. Wang, Y Xu., A. Maisuradze, M. Peschl, D. Tzovaras, “Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction”. Applied Sciences, Vol. 12, Iss. 7, 2022. Available: https://www.mdpi.com/2076-3417/12/7/3218.
44. S. Kumar, D. Goyal, R.K. Dang, S.S. Dhami and B.S. Pabla, “Condition based maintenance of bearings and gears for fault detection - A review”. Materials Today: Proceedings, 2018. DOI 10.1016/j.matpr.2017.12.219.
45. R.B. Randall, “Vibration-based condition monitoring : industrial, aerospace and automotive applications”. John Wiley & Sons Ltd, 2011.
46. A. Galdelli, M. D’Imperio, G. Marchello et al., “A Novel Remote Visual Inspection System for Bridge Predictive Maintenance”. Remote Sensing, Vol. 14, Iss. 10, 2022. Available: https://www.mdpi.com/2072-4292/14/9/2248.
47. A. Dogru, S. Bouarfa, R. Arizar and R. Aydogan, “Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection”. Aerospace, Vol. 7, Iss. 12, 2020. Available: https://www.mdpi.com/2226-4310/7/12/171.
48. R. Rosati, L Romeo., G. Cecchini, F. Tonetto, P. Viti, A. Mancini and E. Frontoni, “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, Vol. 34, 2022. Available: https://link.springer.com/article/10.1007/s10845-022-01960-x.
49. A.K.S. Jardine, D. Lin and D. Banjevic, “A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance”. Mechanical Systems and Signal Processing, Vol. 20, Iss. 7, 2006. Available: https://www.sciencedirect.com/science/article/pii/S0888327005001512.
50. B. Jakubek, K. Grochalski, W. Rukat and H. Sokol, “Thermovision measurements of rolling bearings”. Measurement, Vol. 189, 2022. Available: https://www.sciencedirect.com/science/article/pii/S0263224121013932.

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-026ab881-4192-41d3-8619-201a50b2492e