مرور نظام‌مند فناوری‌های توانمندساز صنعت 4/0 در نت پیش‌بینانه صنایع دفاعی

نویسندگان

  • حسین فیاضی گروه علوم دفاعی راهبردی، دانشگاه عالی دفاع ملی، تهران، ایران.
  • مرتضی پورجعفری گروه علوم دفاعی راهبردی، دانشگاه عالی دفاع ملی، تهران، ایران.
  • نیما صابری‌فرد * گروه پژوهشی آماد و زنجیره‌تامین، دانشگاه دفاع ملی، تهران، ایران. https://orcid.org/0000-0003-1940-7882

https://doi.org/10.22105/mmaa.vi.110

چکیده

هدف: این پژوهش با هدف شناسایی و تحلیل فناوری‌های صنعت 4/0 در حوزه نگهداری پیش‌بینانه صنایع دفاعی و بررسی چالش‌های پیاده‌سازی آن انجام شده است.

روش‌شناسی پژوهش: مطالعه حاضر یک مرور نظام‌مند بر اساس PRISMA 2020 است که از بین ۳۰۰ منبع اولیه، ۱۲ مطالعه نهایی را انتخاب و تحلیل کرده است.

یافته‌ها: اینترنت اشیا و یادگیری ماشین پرکاربردترین فناوری‌ها بودند. اصلی‌ترین چالش‌ها شامل کیفیت داده، امنیت سایبری، محدودیت زیرساخت و کمبود نیروی متخصص است.

اصالت/ارزش افزوده علمی: این پژوهش با تمرکز بر الزامات امنیتی صنعت دفاع، چارچوبی کاربردی برای پیاده‌سازی نگهداری هوشمند ارایه می‌دهد و می‌تواند مبنای تصمیم‌گیری راهبردی در این حوزه باشد.

کلمات کلیدی:

نگهداری پیش‌بینانه ، صنعت ۴٫۰، اینترنت اشیا، یادگیری ماشین، صنایع دفاعی، مرور نظام‌مند

مراجع

  1. [1] Dalzochio, J., Kunst, R., Barbosa, J. L. V., Neto, P. C. D. S., Pignaton, E., Ten Caten, C. S., & da Penha, A. D. L. T. (2023). Predictive maintenance in the military domain: A systematic review of the literature. ACM computing surveys, 55(13s), 1-30. https://doi.org/10.1145/3586100
  2. [2] Nguyen, K. A., Do, P., & Grall, A. (2015). Multi-level predictive maintenance for multi-component systems. Reliability engineering & system safety, 144, 83–94. https://doi.org/10.1016/j.ress.2015.07.017
  3. [3] Wang, J., Zhang, L., Duan, L., & Gao, R. X. (2017). A new paradigm of cloud-based predictive maintenance for intelligent manufacturing. Journal of intelligent manufacturing, 28(5), 1125–1137. https://doi.org/10.1007/s10845-015-1066-0
  4. [4] ur Rehman, M. H., Ahmed, E., Yaqoob, I., Hashem, I. A. T., Imran, M., & Ahmad, S. (2018). Big data analytics in industrial IoT using a concentric computing model. IEEE communications magazine, 56(2), 37–43. https://doi.org/10.1109/MCOM.2018.1700632
  5. [5] Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://www.nature.com/articles/nature16961
  6. [6] Sun, P., Feng, W., Han, R., Yan, S., & Wen, Y. (2019). Optimizing network performance for distributed dnn training on gpu clusters: Imagenet/alexnet training in 1.5 minutes. https://arxiv.org/abs/1902.06855
  7. [7] Wang, H., & Pham, H. (2006). Reliability and Optimal Maintenance of Series Systems with Imperfect Repair and Dependence. In Reliability and optimal maintenance (pp. 91-110). Springer. https://doi.org/10.1007/1-84628-325-6_5
  8. [8] Ponemon Institute. (2011). Cost of data center outages. https://www.ponemon.org/local/upload/file/2011 Cost_of_Data_Center_Outages.pdf
  9. [9] Gong, X., & Qiao, W. (2014). Current-based mechanical fault detection for direct-drive wind turbines via synchronous sampling and impulse detection. IEEE transactions on industrial electronics, 62(3), 1693–1702. https://doi.org/10.1109/TIE.2014.2363440
  10. [10] Bevilacqua, M., & Braglia, M. (2000). The analytic hierarchy process applied to maintenance strategy selection. Reliability engineering & system safety, 70(1), 71–83. https://doi.org/10.1016/S0951-8320(00)00047-8
  11. [11] Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. da P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & industrial engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
  12. [12] Samadzad Ettahdi, S., & Yaghmaei Moghadam, M. H. (2021). Engineering and analysis of big and streaming data in the industrial internet of things (IIoT) and its application in the next generation of predictive maintenance (PdM) systems. The 5th international conference on internet of things and applications, isfahan, Iran. Civilica. (In Persian). https://civilica.com/doc/1238617/
  13. [13] Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical systems and signal processing, 138, 106587. https://doi.org/10.1016/j.ymssp.2019.106587
  14. [14] Li, X., Jiang, H., Hu, Y., & Xiong, X. (2018, August). Intelligent fault diagnosis of rotating machinery based on deep recurrent neural network. In 2018 International conference on Sensing, Diagnostics, prognostics, and control (SDPC) (pp. 67-72). IEEE. https://doi.org/10.1109/SDPC.2018.8664931
  15. [15] Rahmani, M., & Alizade, H. (2021). Applications of artificial intelligence technology in intelligent command and control systems. Iranian Journal of Wargaming, 4(8), 77-101. (In Persian). http://www.ijwg.ir/article_171125.html?lang=en
  16. [16] Zhukabayeva, T., Zholshiyeva, L., Karabayev, N., Khan, S., & Alnazzawi, N. (2025). Cybersecurity solutions for industrial internet of things–edge computing integration: Challenges, threats, and future directions. Sensors, 25(1), 213. https://doi.org/10.3390/s25010213
  17. [17] Bayraktar, O., & Ataç, C. (2018). The effects of Industry 4.0 on human resources management. In Institutions and socio-economic performance: macro and micro perspectives (pp. 337-359). Peter Lang. https://avesis.ticaret.edu.tr/yayin/35b8d867-16ee-4b34-97f4-a48f115b8ecd/the-effects-of-industry-4-0-on-human-resources-management
  18. [18] Schlötzer, F. (2015). Industry 4.0: The world of smart factories [Thesis]. https://research-api.cbs.dk/ws/portalfiles/portal/58442891/Fabian_Schl_tzer.pdf.
  19. [19] Ajli, M., & Saberifard, N. (2021). The emergence of the fourth industrial revolution and the development of digital banking. The first national conference on digital transformation and intelligent systems, Larestan, Iran. Civilica. (In Persian). https://civilica.com/doc/1383965/
  20. [20] Gajek, A., Fabiano, B., Laurent, A., & Jensen, N. (2022). Process safety education of future employee 4.0 in Industry 4.0. Journal of loss prevention in the process industries, 75, 104691. https://doi.org/10.1016/j.jlp.2021.104691
  21. [21] Munera, E., Poza-Lujan, J. L., Posadas-Yagüe, J. L., Simo, J., Blanes, J. F., & Albertos, P. (2015). Control kernel in smart factory environments: Smart resources integration. 2015 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER) (pp. 2002–2005). IEEE. https://doi.org/10.1109/CYBER.2015.7288255
  22. [22] Fernandez-Carames, T. M., & Fraga-Lamas, P. (2019). A review on the application of blockchain to the next generation of cybersecure Industry 4.0 smart factories. IEEE access, 7, 45201–45218. https://doi.org/10.1109/ACCESS.2019.2908780
  23. [23] Scaife, A. D. (2024). Improve predictive maintenance through the application of artificial intelligence: A systematic review. Results in engineering, 21, 101645. https://doi.org/10.1016/j.rineng.2023.101645
  24. [24] Arafat, M. Y., Hossain, M. J., & Alam, M. M. (2024). Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects. Renewable and sustainable energy reviews, 190, 114088. https://doi.org/10.1016/j.rser.2023.114088
  25. [25] Feldmann, S. (2018). Predictive maintenance–from data collection to value creation. https://www.rolandberger.com/en/Insights/Publications/Predictive-maintenance-from-data-collection-to-value-creation.html
  26. [26] Mallioris, P., Aivazidou, E., & Bechtsis, D. (2024). Predictive maintenance in Industry 4.0: A systematic multi-sector mapping. CIRP journal of manufacturing science and technology, 50, 80–103. https://doi.org/10.1016/j.cirpj.2024.02.003
  27. [27] Pióro, Ł., Sychowiec, J., Kanciak, K., & Zieliński, Z. (2024). Application of attribute-based encryption in military internet of things environment. Sensors, 24(18), 5863. https://doi.org/10.3390/s24185863
  28. [28] Green, M. (2024). The use of big data in predictive maintenance for aerospace systems. American journal of aerospace and aeronautical engineering, 5(6), 11–15. https://australiansciencejournals.com/ajaae/article/view/536
  29. [29] Madusanka, N. S., Fan, Y., Yang, S., & Xiang, X. (2023). Digital twin in the maritime domain: A review and emerging trends. Journal of marine science and engineering, 11(5), 1021. https://doi.org/10.3390/jmse11051021
  30. [30] Sengupta, P., Mehta, A., & Rana, P. S. (2023). Predictive maintenance of armoured vehicles using machine learning approaches. https://arxiv.org/abs/2307.14453
  31. [31] Pasdar, A., Koroniotis, N., Keshk, M., Moustafa, N., & Tari, Z. (2024). Cybersecurity solutions and techniques for internet of things integration in combat systems. IEEE transactions on sustainable computing, 10(2), 345–365. https://doi.org/10.1109/TSUSC.2024.3443256
  32. [32] Salhaoui, M. (2021). Smart IoT monitoring and real-time control based on autonomous robots, visual recognition and cloud/edge computing services [Thesis]. https://doi.org/10.31428/10317/10416
  33. [33] Scott, M. J., Verhagen, W. J. C., Bieber, M. T., & Marzocca, P. (2022). A systematic literature review of predictive maintenance for defence fixed-wing aircraft sustainment and operations. Sensors, 22(18), 7070. https://doi.org/10.3390/s22187070
  34. [34] Moghtadaei, A. (2024). Aircraft engine maintenance and digital Twin technology in aircraft engines. J data analytic eng decision making, 1(1), 1–5. https://www.researchgate.net/profile/Anahita-Moghtadaei/publication/381587777
  35. [35] Dalzochio, J., Kunst, R., Barbosa, J., De Freitas, E. P., & Binotto, A. (2023). MILPdM: A predictive maintenance architecture for the military domain. 2023 international conference on machine learning and applications (ICMLA) (pp. 2026–2032). IEEE. https://doi.org/10.1109/ICMLA58977.2023.00306
  36. [36] Kciuk, S., Krzystała, E., Mkeżyk, A., & Szmidt, P. (2021). The application of microelectromechanical systems (MEMS) accelerometers to the assessment of blast threat to armored vehicle crew. Sensors, 22(1), 316. https://doi.org/10.3390/s22010316
  37. [37] Frazier, W. (2022). Predictive maintenance using machine learning and existing data sources [Thesis].
  38. [38] Gholam Nejad, P., Gholami, M., & Pourmakary, A. (2019). Military applications of the internet of things, with emphasis on IR Iran Air Force missions. Military science and tactics, 15(49), 141-163. (In Persian). https://doi.org/10.22034/qjmst.2019.38084
  39. [39] Zeb, S., & Lodhi, S. K. (2025). AI for predictive maintenance: Reducing downtime and enhancing efficiency. Enrichment: journal of multidisciplinary research and development, 3(1), 135–150. https://doi.org/10.55324/enrichment.v3i1.338
  40. [40] Kolo, F. H. O. (2025). From framework to practice: Barriers and enablers to RMF adoption in mid-sized enterprises. Asian journal of research in computer science, 18(5), 459–479. https://doi.org/10.9734/ajrcos/2025/v18i5667
  41. [41] Beheshti Atashgah, M., Barari, M., Bayat, M., & Aref, M. R. (2018). Concepts and security challenges of the military internet of things, focusing on the United States MIoT mechanism. Command and control quarterly, 2(3), 64-78. (In Persian). https://ic4i-journal.ir/browse.php?a_id=93&sid=1&slc_lang=fa
  42. [42] Alinezhad, A., Azar, D., & Sajadi, V. (2023). Application of internet of things technology in cyber and electronic warfare. Military science and tactics, 19(65), 29–68. (In Persian). https://www.qjmst.ir/article_710159_en.html?lang=fa#:~:text=10.22034/qjmst.2024.556192.1738
  43. [43] Mozaffari, M. M., & Saberifard, N. (2016). A dictionary of abbreviations in management. Qazvin Academic Jihad Publications. (In Persian). https://b2n.ir/zn5794

دانلود

چاپ شده

2025-11-03

شماره

دوره 2 شماره 4 (2025): مدیریت: مدلسازی، تحلیل‌ها و کاربردها

نوع مقاله

مقالات شماره جاری

ارجاع به مقاله

فیاضی ح., پورجعفری م., & صابری‌فرد ن. (2025). مرور نظام‌مند فناوری‌های توانمندساز صنعت 4/0 در نت پیش‌بینانه صنایع دفاعی. مدیریت: مدلسازی، تحلیل‌ها و کاربرد, 2(4), 253-268. https://doi.org/10.22105/mmaa.vi.110
Crossref
Scopus
Google Scholar
Europe PMC

مقالات مشابه

##common.pagination##

همچنین برای این مقاله می‌توانید شروع جستجوی پیشرفته مقالات مشابه.