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Advancing predictive maintenance: a comprehensive case study through industry 4.0

Yıl 2024, , 133 - 142, 30.09.2024
https://doi.org/10.18245/ijaet.1543509

Öz

The paper is focused on predictive maintenance in the automotive industry with a specialization in technologies related to Industry 4.0. The emergence of predictive maintenance with Industry 4.0 technologies is also called Maintenance 4.0. The article describes specific Industry 4.0 technologies, such as supervisory control and data acquisition (SCADA) and Predictive Maintenance (PdM), that are being implemented in manufacturing companies. The sample for the case study consisted of a paint shop from the automotive sector that operates in Turkey. In the data collection step, SCADA collected data from pressure sensors to create a historical database for PdM. The purpose of this article is to illustrate, through a case study, the importance of PdM using SCADA using Industry 4.0 instruments such as the Internet of Things and big data. 18 filters, which were replaced every 6 months during preventive maintenance, are now replaced at different intervals through PDM after this study. Based on SCADA data, a total of 2 filters experience blockage in both the primer and topcoat paint processes every 6 months. Over a 12-month period, an extra 9 filters are blocked across all processes. Furthermore, within a 24-month timeframe, a total of 7 more filters become blocked in the entirety of the operational processes. The results showed that the PdM combination increased the effectiveness of care by 45.83%.

Kaynakça

  • Ceruti A, Marzocca P, Liverani A, Bil C, Maintenance in aeronautics in an industry 4.0 context: The role of augmented reality and additive manufacturing. J Comput Des Eng, 6(4), 516-526, 2019.
  • Lee M, Yun J, Pyka A, Won D, Kodama F, Schiuma G, Yan MR, How to respond to the Fourth Industrial Revolution, or the Second Information Technology Revolution? Dynamic new combinations between technology, market, and society through open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 4(3), 1-21, 2018.
  • Tjahjono B, Esplugues C, Ares E, Pelaez G, What does Industry 4.0 mean to supply chain? Procedia Manuf, 13, 1175-1182, 2017.
  • Bartodziej CJ The concept Industry 4.0. In The Concept Industry 4.0. Springer Gabler: Wiesbaden, 2017.
  • Chiu YC, Cheng FT, Huang HC, Developing a factory-wide intelligent predictive maintenance system based on Industry 4.0. Journal of the Chinese Institute of Engineers, 40(7), 562-571, 2017.
  • Chung M, Kim J, The internet information and technology research directions based on the fourth industrial revolution. KSII Transactions on Internet, Information Systems, 10(3), 1311-1320, 2016.
  • Kaczmarek M, Gola A, Maintenance 4.0 technologies for sustainable manufacturing an overview. IFAC-PapersOnLine, 52(10), 91-96, 2019.
  • Carnero, M. C., Selection of diagnostic techniques and instrumentation in a predictive maintenance program: A case study. Decision support systems, 38(4), 539-555, 2005.
  • Gupta R, Kumar A., Integration of big data and artificial intelligence in predictive maintenance: A review. Int J Adv Manuf Technol, 40(1), 56-78, 2022.
  • Jones A, Brown M., Advances in predictive maintenance technologies. International Journal of Reliability Engineering and Asset Management, 15(2), 321-345, 2020.
  • Sahal R, Breslin JG, Ali MI, Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. J Manuf Syst, 54, 138-151, 2020.
  • Ruiz-Sarmiento JR, Monroy J, Moreno FA, Galindo C, Bonelo JM, Gonzalez-Jimenez J., A predictive model for the maintenance of industrial machinery in the context of Industry 4.0. Eng Appl Artif Intell, 87, 103289, 2020.
  • Smith J., A history of maintenance: From ancient civilizations to the industrial revolution. Journal of Maintenance History, 25(2), 123-145, 2000.
  • Cachada A, Barbosa J, Leitño P, Gcraldcs CA, Deusdado L, Costa J, Romero L., Maintenance 4.0: Intelligent and predictive maintenance system architecture. In 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Vol. 1, 139-146, 2018.
  • Kiangala KS, Wang Z., Initiating predictive maintenance for a conveyor motor in a bottling plant using Industry 4.0 concepts. Int J Adv Manuf Technol, 97(9-12), 3251-3271, 2018.
  • Sakib N, Wuest T., Challenges and opportunities of condition-based predictive maintenance: A review. Procedia CIRP, 78, 267-272, 2018.
  • Ferreiro S, Konde E, Fernández S, Prado A., Industry 4.0: predictive intelligent maintenance for production equipment. In European Conference of the Prognostics and Health Management Society, 1-8, 2016.
  • Einabadi B, Baboli A, Ebrahimi M., Dynamic predictive maintenance in Industry 4.0 based on real time information: Case study in automotive industries. IFAC-PapersOnLine, 52(13), 1069-1074, 2019.
  • Pophaley M, Vyas RK., Choice criteria for maintenance strategy in automotive industries. International Journal of Management Science and Engineering Management, 5(6), 446-452, 2010.
  • Gallab M, Bouloiz H, Garbolino E, Tkiouat M, ElKilani MA, Bureau N., Risk analysis of maintenance activities in a LPG supply chain with a Multi-Agent approach. J Loss Prev Process Ind, 47, 41-56, 2017. Sharma A, Yadava G, Deshmukh S., A literature review and future perspectives on maintenance optimisation. J Qual Maint Eng, 17(1), 5-25, 2011.
  • Achouch M, Dimitrova M, Ziane K, Sattarpanah Karganroudi S, Dhouib R, Ibrahim H, Adda M., On predictive maintenance in Industry 4.0: Overview, models, and challenges. Appl Sci, 12(16), 8081, 1-22, 2022.
  • Creehan KD., Establishing optimal maintenance practices in a traditional manufacturing environment. Journal of the Chinese Institute of Industrial Engineers, 22(1), (2005) 11-18, 2005.
  • Lee J, Ardakani HD, Yang S, Bagheri B., Industrial big data analytics and cyber-physical systems for future maintenance, service innovation. Procedia CIRP, 38, 3-7, 2015.
  • Patwardhan A, Verma AK, Kumar U., A survey on predictive maintenance through big data. Current Trends in Reliability, Availability, Maintainability and Safety, 437-445, 2016.
  • Fernández del Amo I, Erkoyuncu J, Roy R, Palmarini R, Onoufriou D., A systematic review of augmented reality content-related techniques for knowledge transfer in maintenance applications. Comput Ind, 103, 47-71, 2018.
  • Silvestri L, Forcina A, Introna V, Santolamazza A, Cesarotti V., Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Comput Ind, 123, 103335, 2020.
  • Zhang W, Yang D, Wang H., Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Syst J, 13(3), 2213-2227, 2019.
  • Kiangala KS, Wang Z., An Industry 4.0 approach to develop auto parameter configuration of a bottling process in a small to medium scale industry using PLC and SCADA. Procedia Manuf, 35, 725-730, 2019.
  • Banica CF, Belu N., Application of 8D methodology-an effective problem solving tool in automotive industry. Scientific Bulletin Automotive Series, XXV (29), 1-7, 2019.
  • Barosani S, Bhalwankar N, Deshmukh V, Kokane S, Kulkarni PR., A review on 8D problem solving process, Int J Res Appl Sci Eng Technol, 4 (4), 529-535, 2017. MDHS 14/3 General methods for sampling and gravimetric analysis of respirable and inhalable dust https://online.br.sgs.com/Extranet/Environ/web/pdf/particulados.pdf access , 20.07.2023.
Yıl 2024, , 133 - 142, 30.09.2024
https://doi.org/10.18245/ijaet.1543509

Öz

Kaynakça

  • Ceruti A, Marzocca P, Liverani A, Bil C, Maintenance in aeronautics in an industry 4.0 context: The role of augmented reality and additive manufacturing. J Comput Des Eng, 6(4), 516-526, 2019.
  • Lee M, Yun J, Pyka A, Won D, Kodama F, Schiuma G, Yan MR, How to respond to the Fourth Industrial Revolution, or the Second Information Technology Revolution? Dynamic new combinations between technology, market, and society through open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 4(3), 1-21, 2018.
  • Tjahjono B, Esplugues C, Ares E, Pelaez G, What does Industry 4.0 mean to supply chain? Procedia Manuf, 13, 1175-1182, 2017.
  • Bartodziej CJ The concept Industry 4.0. In The Concept Industry 4.0. Springer Gabler: Wiesbaden, 2017.
  • Chiu YC, Cheng FT, Huang HC, Developing a factory-wide intelligent predictive maintenance system based on Industry 4.0. Journal of the Chinese Institute of Engineers, 40(7), 562-571, 2017.
  • Chung M, Kim J, The internet information and technology research directions based on the fourth industrial revolution. KSII Transactions on Internet, Information Systems, 10(3), 1311-1320, 2016.
  • Kaczmarek M, Gola A, Maintenance 4.0 technologies for sustainable manufacturing an overview. IFAC-PapersOnLine, 52(10), 91-96, 2019.
  • Carnero, M. C., Selection of diagnostic techniques and instrumentation in a predictive maintenance program: A case study. Decision support systems, 38(4), 539-555, 2005.
  • Gupta R, Kumar A., Integration of big data and artificial intelligence in predictive maintenance: A review. Int J Adv Manuf Technol, 40(1), 56-78, 2022.
  • Jones A, Brown M., Advances in predictive maintenance technologies. International Journal of Reliability Engineering and Asset Management, 15(2), 321-345, 2020.
  • Sahal R, Breslin JG, Ali MI, Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. J Manuf Syst, 54, 138-151, 2020.
  • Ruiz-Sarmiento JR, Monroy J, Moreno FA, Galindo C, Bonelo JM, Gonzalez-Jimenez J., A predictive model for the maintenance of industrial machinery in the context of Industry 4.0. Eng Appl Artif Intell, 87, 103289, 2020.
  • Smith J., A history of maintenance: From ancient civilizations to the industrial revolution. Journal of Maintenance History, 25(2), 123-145, 2000.
  • Cachada A, Barbosa J, Leitño P, Gcraldcs CA, Deusdado L, Costa J, Romero L., Maintenance 4.0: Intelligent and predictive maintenance system architecture. In 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Vol. 1, 139-146, 2018.
  • Kiangala KS, Wang Z., Initiating predictive maintenance for a conveyor motor in a bottling plant using Industry 4.0 concepts. Int J Adv Manuf Technol, 97(9-12), 3251-3271, 2018.
  • Sakib N, Wuest T., Challenges and opportunities of condition-based predictive maintenance: A review. Procedia CIRP, 78, 267-272, 2018.
  • Ferreiro S, Konde E, Fernández S, Prado A., Industry 4.0: predictive intelligent maintenance for production equipment. In European Conference of the Prognostics and Health Management Society, 1-8, 2016.
  • Einabadi B, Baboli A, Ebrahimi M., Dynamic predictive maintenance in Industry 4.0 based on real time information: Case study in automotive industries. IFAC-PapersOnLine, 52(13), 1069-1074, 2019.
  • Pophaley M, Vyas RK., Choice criteria for maintenance strategy in automotive industries. International Journal of Management Science and Engineering Management, 5(6), 446-452, 2010.
  • Gallab M, Bouloiz H, Garbolino E, Tkiouat M, ElKilani MA, Bureau N., Risk analysis of maintenance activities in a LPG supply chain with a Multi-Agent approach. J Loss Prev Process Ind, 47, 41-56, 2017. Sharma A, Yadava G, Deshmukh S., A literature review and future perspectives on maintenance optimisation. J Qual Maint Eng, 17(1), 5-25, 2011.
  • Achouch M, Dimitrova M, Ziane K, Sattarpanah Karganroudi S, Dhouib R, Ibrahim H, Adda M., On predictive maintenance in Industry 4.0: Overview, models, and challenges. Appl Sci, 12(16), 8081, 1-22, 2022.
  • Creehan KD., Establishing optimal maintenance practices in a traditional manufacturing environment. Journal of the Chinese Institute of Industrial Engineers, 22(1), (2005) 11-18, 2005.
  • Lee J, Ardakani HD, Yang S, Bagheri B., Industrial big data analytics and cyber-physical systems for future maintenance, service innovation. Procedia CIRP, 38, 3-7, 2015.
  • Patwardhan A, Verma AK, Kumar U., A survey on predictive maintenance through big data. Current Trends in Reliability, Availability, Maintainability and Safety, 437-445, 2016.
  • Fernández del Amo I, Erkoyuncu J, Roy R, Palmarini R, Onoufriou D., A systematic review of augmented reality content-related techniques for knowledge transfer in maintenance applications. Comput Ind, 103, 47-71, 2018.
  • Silvestri L, Forcina A, Introna V, Santolamazza A, Cesarotti V., Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Comput Ind, 123, 103335, 2020.
  • Zhang W, Yang D, Wang H., Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Syst J, 13(3), 2213-2227, 2019.
  • Kiangala KS, Wang Z., An Industry 4.0 approach to develop auto parameter configuration of a bottling process in a small to medium scale industry using PLC and SCADA. Procedia Manuf, 35, 725-730, 2019.
  • Banica CF, Belu N., Application of 8D methodology-an effective problem solving tool in automotive industry. Scientific Bulletin Automotive Series, XXV (29), 1-7, 2019.
  • Barosani S, Bhalwankar N, Deshmukh V, Kokane S, Kulkarni PR., A review on 8D problem solving process, Int J Res Appl Sci Eng Technol, 4 (4), 529-535, 2017. MDHS 14/3 General methods for sampling and gravimetric analysis of respirable and inhalable dust https://online.br.sgs.com/Extranet/Environ/web/pdf/particulados.pdf access , 20.07.2023.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Otomotiv Mühendisliği ve Malzemeleri
Bölüm Article
Yazarlar

Ülge Taş 0000-0002-2376-3735

Erken Görünüm Tarihi 29 Eylül 2024
Yayımlanma Tarihi 30 Eylül 2024
Gönderilme Tarihi 4 Eylül 2024
Kabul Tarihi 23 Eylül 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Taş, Ü. (2024). Advancing predictive maintenance: a comprehensive case study through industry 4.0. International Journal of Automotive Engineering and Technologies, 13(3), 133-142. https://doi.org/10.18245/ijaet.1543509
AMA Taş Ü. Advancing predictive maintenance: a comprehensive case study through industry 4.0. International Journal of Automotive Engineering and Technologies. Eylül 2024;13(3):133-142. doi:10.18245/ijaet.1543509
Chicago Taş, Ülge. “Advancing Predictive Maintenance: A Comprehensive Case Study through Industry 4.0”. International Journal of Automotive Engineering and Technologies 13, sy. 3 (Eylül 2024): 133-42. https://doi.org/10.18245/ijaet.1543509.
EndNote Taş Ü (01 Eylül 2024) Advancing predictive maintenance: a comprehensive case study through industry 4.0. International Journal of Automotive Engineering and Technologies 13 3 133–142.
IEEE Ü. Taş, “Advancing predictive maintenance: a comprehensive case study through industry 4.0”, International Journal of Automotive Engineering and Technologies, c. 13, sy. 3, ss. 133–142, 2024, doi: 10.18245/ijaet.1543509.
ISNAD Taş, Ülge. “Advancing Predictive Maintenance: A Comprehensive Case Study through Industry 4.0”. International Journal of Automotive Engineering and Technologies 13/3 (Eylül 2024), 133-142. https://doi.org/10.18245/ijaet.1543509.
JAMA Taş Ü. Advancing predictive maintenance: a comprehensive case study through industry 4.0. International Journal of Automotive Engineering and Technologies. 2024;13:133–142.
MLA Taş, Ülge. “Advancing Predictive Maintenance: A Comprehensive Case Study through Industry 4.0”. International Journal of Automotive Engineering and Technologies, c. 13, sy. 3, 2024, ss. 133-42, doi:10.18245/ijaet.1543509.
Vancouver Taş Ü. Advancing predictive maintenance: a comprehensive case study through industry 4.0. International Journal of Automotive Engineering and Technologies. 2024;13(3):133-42.