Digitalization in Industrial Facilities and the Classification of Maintenance Types: A Review

Öz

The digitalization of industrial facilities, driven by Industry 4.0 technologies, is fundamentally changing how production assets are monitored, modeled, and managed, and its strongest effect is felt in maintenance management, where traditional corrective and time-based practices are giving way to data-driven, condition-based, and predictive maintenance strategies. This review addresses two linked questions: how industrial maintenance types can be organized within a consistent framework, and how digital enablers such as cyber-physical systems, the Industrial Internet of Things (IIoT), the digital twin, machine learning, and remaining-useful-life (RUL) estimation reshape these types. The study is designed as a narrative (non-systematic) review of seventy-one peer-reviewed journal articles published between 1995 and 2026, complemented by the primary sources of the reliability centered maintenance literature. Maintenance strategies are classified and compared along a reactive–proactive axis as corrective, planned/periodic, condition-based, predictive, reliability-centered (RCM), risk-based (RBM), and opportunistic maintenance. We show that digitalization repositions these types within a continuous condition-monitoring–diagnosis–prognosis–decision loop. The review also surveys recent developments (large language models and generative artificial intelligence, federated and edge learning, explainable AI, digital-twin interoperability standards, and the human-centric maintenance perspective of Industry 4.0) and discusses their implications for maintenance management. As an original contribution, we propose a conceptual digital-maintenance framework that couples the digital twin, RUL-based maintenance-interval computation, and an expected profit-loss indicator (ePLI) with a reminder/alarm decision layer; the ePLI is formalized through an expected-loss expression, a threshold-based reminder/alarm rule, and an illustrative application scenario. The findings indicate that digitalization turns maintenance types from mutually exclusive choices into elements of an integrated asset-management continuum, and that Industry 4.0 and trustworthy-AI research adds a human-centric dimension to this continuum. For practitioners, the review offers a stepwise roadmap for migrating from corrective and time-based practice toward predictive, economically justified maintenance.

Anahtar Kelimeler

• Digitalization
• ; Maintenance management
• Predictive maintenance
• Digital twin
• Industry 4.0

Endüstriyel Tesislerde Dijitalleşme ve Bakım Türlerinin Sınıflandırılması: Bir Derleme

Öz

Endüstriyel tesislerin Endüstri 4.0 teknolojileriyle dijitalleşmesi, üretim varlıklarının izlenmesi, modellenmesi ve yönetilme biçimlerini temelden değiştirmekte; bu dönüşümün en güçlü etkilerinden biri, geleneksel düzeltici ve zamana dayalı bakım uygulamalarının yerini veri odaklı, duruma dayalı ve kestirimci bakım stratejilerine bıraktığı bakım yönetimi alanında görülmektedir. Bu derleme, birbiriyle ilişkili iki temel soruya odaklanmaktadır: Endüstriyel bakım türleri tutarlı bir çerçeve içerisinde nasıl sınıflandırılabilir ve siber-fiziksel sistemler, Endüstriyel Nesnelerin İnterneti (IIoT), dijital ikiz, makine öğrenmesi ve kalan faydalı ömür (RUL) tahmini gibi dijital teknolojiler bu bakım türlerini nasıl dönüştürmektedir? Çalışma, 1995–2026 yılları arasında yayımlanmış 71 hakemli dergi makalesinin yanı sıra güvenilirlik merkezli bakım (RCM) literatürünün temel kaynaklarını kapsayan anlatı türünde (sistematik olmayan) bir derleme olarak tasarlanmıştır. Bakım stratejileri; reaktif–proaktif ekseninde düzeltici bakım, planlı/periyodik bakım, koşula dayalı bakım, kestirimci bakım, güvenilirlik merkezli bakım (RCM), risk temelli bakım (RBM) ve fırsatçı bakım olmak üzere sınıflandırılmış ve karşılaştırılmıştır. Bulgular, dijitalleşmenin bu bakım türlerini sürekli durum izleme–teşhis–öngörü–karar verme döngüsü içerisinde yeniden konumlandırdığını göstermektedir. Ayrıca çalışmada; büyük dil modelleri ve üretken yapay zekâ, federatif ve uçta öğrenme, açıklanabilir yapay zekâ, dijital ikizler arası birlikte çalışabilirlik standartları ve Endüstri 4.0'ın insan merkezli bakım yaklaşımı gibi güncel gelişmeler incelenmiş ve bunların bakım yönetimine etkileri tartışılmıştır. Çalışmanın özgün katkısı olarak; dijital ikiz, RUL tabanlı bakım aralığı hesaplaması ve beklenen kâr-kayıp göstergesini (ePLI) bir hatırlatma/alarm karar katmanıyla birleştiren kavramsal bir dijital bakım çerçevesi önerilmiştir. ePLI; beklenen kayıp ifadesi, eşik tabanlı hatırlatma/alarm kuralı ve örnek bir uygulama senaryosu aracılığıyla formüle edilmiştir. Bulgular, dijitalleşmenin bakım türlerini birbirinden bağımsız seçenekler olmaktan çıkararak entegre bir varlık yönetimi sürekliliğinin unsurları hâline getirdiğini ve Endüstri 4.0 ile güvenilir yapay zekâ araştırmalarının bu sürekliliğe insan merkezli yeni bir boyut kazandırdığını göstermektedir. Uygulayıcılar açısından çalışma, düzeltici ve zamana dayalı bakım uygulamalarından kestirimci ve ekonomik açıdan gerekçelendirilmiş bakım yaklaşımına geçiş için aşamalı bir yol haritası sunmaktadır.

Anahtar Kelimeler

• Dijitalizasyon
• Bakım Yönetimi
• Kestirimci Bakım
• Dijital İkiz
• Endüstri 4.0

Kaynakça

1. [1] Mueller, E., Chen, X. L., & Riedel, R. (2017). Challenges and requirements for the application of Industry 4.0: A special insight with the usage of cyber-physical system. Chinese Journal of Mechanical Engineering, 30(5), 1050–1057. https://doi.org/10.1007/s10033-017-0164-7
2. [2] El Kadiri, S., Grabot, B., Thoben, K.-D., Hribernik, K., Emmanouilidis, C., von Cieminski, G., & Kiritsis, D. (2016). Current trends on ICT technologies for enterprise information systems. Computers in Industry, 79, 14–33. https://doi.org/10.1016/j.compind.2015.06.008
3. [3] Ghobakhloo, M., & Iranmanesh, M. (2021). Digital transformation success under Industry 4.0: A strategic guideline for manufacturing SMEs. Journal of Manufacturing Technology Management, 32(8), 1533–1556. https://doi.org/10.1108/JMTM-11-2020-0455
4. [4] Ghobakhloo, M., & Fathi, M. (2019). Corporate survival in Industry 4.0 era: The enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management, 31(1), 1–30. https://doi.org/10.1108/JMTM-11-2018-0417
5. [5] Savastano, M., Amendola, C., Bellini, F., & D’Ascenzo, F. (2019). Contextual impacts on industrial processes brought by the digital transformation of manufacturing: A systematic review. Sustainability, 11(3), Article 891. https://doi.org/10.3390/su11030891
6. [6] Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
7. [7] Gackowiec, P. (2019). General overview of maintenance strategies–concepts and approaches. Multidisciplinary Aspects of Production Engineering, 2.
8. [8] Eti, M. C., Ogaji, S. O. T., & Probert, S. D. (2006). Development and implementation of preventive-maintenance practices in Nigerian industries. Applied energy, 83(10), 1163-1179. https://doi.org/10.1016/j.apenergy.2006.01.001

9. [9] Chin, H. H., Varbanov, P. S., Klemeš, J. J., Benjamin, M. F. D., & Tan, R. R. (2020). Asset maintenance optimisation approaches in the chemical and process industries–A review. Chemical Engineering Research and Design, 164, 162-194. https://doi.org/10.1016/j.cherd.2020.09.034
10. [10] W. Qingfeng, L. Wenbin, Z. Xin, Y. Jianfeng, and Y. Qingbin, “Development and application of equipment maintenance and safety integrity management system,” Journal of Loss Prevention in the Process Industries, vol. 24, no. 4, pp. 321–332, 2011,
11. [11] Vijay Kumar, E., Chaturvedi, S. K., & Deshpandé, A. W. (2009). Maintenance of industrial equipment. International Journal of Quality & Reliability Management, 26(2), 196-211. https://doi.org/10.1108/02656710910928824
12. [12] Cigolini, R., Fedele, L., Garetti, M., & Macchi, M. (2008). Recent advances in maintenance and facility management. Production Planning & Control, 19(4), 279-286. https://doi.org/10.1080/09537280802034034
13. [13] Koulamas, C., & Kalogeras, A. (2018). Cyber-Physical Systems and Digital Twins in the Industrial Internet of Things [Cyber-Physical Systems]. Computer, 51(11), 95-98. https://doi.org/10.1109/mc.2018.2876181
14. [14] Andronie, M., Lăzăroiu, G., Ștefănescu, R., Uță, C., & Dijmărescu, I. (2021). Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review. Sustainability, 13(10), 5495. https://doi.org/10.3390/su13105495
15. [15] Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access, 8, 108952-108971. https://doi.org/10.1109/access.2020.2998358
16. [16] He, B., & Bai, K. (2020). Digital twin-based sustainable intelligent manufacturing: a review. Advances in Manufacturing, 9(1), 1-21. https://doi.org/10.1007/s40436-020-00302-5
17. [17] Medjaher, K., Tobon-Mejia, D. A., & Zerhouni, N. (2012). Remaining Useful Life Estimation of Critical Components With Application to Bearings. IEEE Transactions on Reliability, 61(2), 292-302. https://doi.org/10.1109/tr.2012.2194175
18. [18] Ahmed Murtaza, A., Saher, A., Hamza Zafar, M., Kumayl Raza Moosavi, S., Faisal Aftab, M., & Sanfilippo, F. (2024). Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study. Results in Engineering, 24, 102935. https://doi.org/10.1016/j.rineng.2024.102935
19. [19] Psarommatis, F., May, G., & Azamfirei, V. (2023). Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework. Journal of Manufacturing Systems, 68, 376-399. https://doi.org/10.1016/j.jmsy.2023.04.009
20. [20] Li, Y., Wang, H., & Sun, M. (2024). ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps. Reliability Engineering & System Safety, 243, 109850. https://doi.org/10.1016/j.ress.2023.109850
21. [21] Ucar, A., Karakose, M., & Kırımça, N. (2024). Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends. Applied Sciences, 14(2), 898. https://doi.org/10.3390/app14020898
22. [22] Liu, B., Zhao, X., Liu, Y., & Do, P. (2020). Maintenance optimisation for systems with multi-dimensional degradation and imperfect inspections. International Journal of Production Research, 59(24), 7537-7559. https://doi.org/10.1080/00207543.2020.1844919
23. [23] Lind, S., Nenonen, S., & Kivistö‐Rahnasto, J. (2008). Safety risk assessment in industrial maintenance. Journal of Quality in Maintenance Engineering, 14(2), 205-217. https://doi.org/10.1108/13552510810877692
24. [24] Shafiee, M., & Sørensen, J. D. (2019). Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies. Reliability Engineering & System Safety, 192, 105993. https://doi.org/10.1016/j.ress.2017.10.025
25. [25] Zhao, J., Gao, C., & Tang, T. (2022). A Review of Sustainable Maintenance Strategies for Single Component and Multicomponent Equipment. Sustainability, 14(5), 2992. https://doi.org/10.3390/su14052992
26. [26] Ulansky, V., & Raza, A. (2023). Classification of Systems and Maintenance Models. Aerospace, 10(5), 456. https://doi.org/10.3390/aerospace10050456
27. [27] Geisbush, J., & Ariaratnam, S. T. (2022). Reliability centered maintenance (RCM): literature review of current industry state of practice. Journal of Quality in Maintenance Engineering, 29(2), 313-337. https://doi.org/10.1108/jqme-02-2021-0018
28. [28] Derigent, W., Thomas, E., Levrat, E., & Iung, B. (2009). Opportunistic maintenance based on fuzzy modelling of component proximity. CIRP Annals, 58(1), 29-32. https://doi.org/10.1016/j.cirp.2009.03.079
29. [29] Li, X., Ran, Y., & Zhang, G. (2021). Optimization of equal-cycle maintenance strategy considering imperfect preventive maintenance. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 236(4), 1392-1402. https://doi.org/10.1177/09544089211063852
30. [30] Tsang, A. H. C. (1995). Condition‐based maintenance: tools and decision making. Journal of Quality in Maintenance Engineering, 1(3), 3-17. https://doi.org/10.1108/13552519510096350
31. [31] Nowlan, F. S., & Heap, H. F. (1978). Reliability-centered maintenance (Report No. AD-A066579). United Air Lines, Inc.; U.S. Department of Defense. https://www.omdec.com/wikifiles/nowlanHeap.pdf
32. [32] Moubray, J. (1997). Reliability-centred maintenance (RCM II) (2nd ed.). Butterworth-Heinemann.
33. [33] G-11M, Maintainability, Supportability and Logistics,. (n.d.). Evaluation Criteria for Reliability-Centered Maintenance (RCM) Processes. SAE International. https://doi.org/10.4271/ja1011_200908
34. [34] Koenig, F., Found, P. A., & Kumar, M. (2019). Innovative airport 4.0 condition-based maintenance system for baggage handling DCV systems. International Journal of Productivity and Performance Management, 68(3), 561-577. https://doi.org/10.1108/ijppm-04-2018-0136
35. [35] Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778. https://doi.org/10.1080/00207543.2021.1956675
36. [36] Meddaoui, A., Hain, M., & Hachmoud, A. (2023). The benefits of predictive maintenance in manufacturing excellence: a case study to establish reliable methods for predicting failures. The International Journal of Advanced Manufacturing Technology, 128(7-8), 3685-3690. https://doi.org/10.1007/s00170-023-12086-6
37. [37] Turnbull, A., & Carroll, J. (2021). Cost Benefit of Implementing Advanced Monitoring and Predictive Maintenance Strategies for Offshore Wind Farms. Energies, 14(16), 4922. https://doi.org/10.3390/en14164922
38. [38] Liu, C., Tang, D., Zhu, H., & Nie, Q. (2021). A Novel Predictive Maintenance Method Based on Deep Adversarial Learning in the Intelligent Manufacturing System. IEEE Access, 9, 49557-49575. https://doi.org/10.1109/access.2021.3069256
39. [39] Sang, G. M., Xu, L., & de Vrieze, P. (2021). A Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0. Frontiers in Big Data, 4. https://doi.org/10.3389/fdata.2021.663466
40. [40] Adu-Amankwa, K., Attia, A. K. A., Janardhanan, M. N., & Patel, I. (2019). A predictive maintenance cost model for CNC SMEs in the era of industry 4.0. The International Journal of Advanced Manufacturing Technology, 104(9-12), 3567-3587. https://doi.org/10.1007/s00170-019-04094-2
41. [41] Lee, S. M., Lee, D., & Kim, Y. S. (2019). The quality management ecosystem for predictive maintenance in the Industry 4.0 era. International Journal of Quality Innovation, 5(1). https://doi.org/10.1186/s40887-019-0029-5
42. [42] Gupta, G., & Mishra, R. P. (2016). A SWOT analysis of reliability centered maintenance framework. Journal of Quality in Maintenance Engineering, 22(2), 130-145. https://doi.org/10.1108/jqme-01-2015-0002
43. [43] Piasson, D., Bíscaro, A. A. P., Leão, F. B., & Mantovani, J. R. S. (2016). A new approach for reliability-centered maintenance programs in electric power distribution systems based on a multiobjective genetic algorithm. Electric Power Systems Research, 137, 41-50. https://doi.org/10.1016/j.epsr.2016.03.040
44. [44] Zakikhani, K., Nasiri, F., & Zayed, T. (2020). Availability-based reliability-centered maintenance planning for gas transmission pipelines. International Journal of Pressure Vessels and Piping, 183, 104105. https://doi.org/10.1016/j.ijpvp.2020.104105
45. [45] Braglia, M., Castellano, D., & Gallo, M. (2019). A novel operational approach to equipment maintenance: TPM and RCM jointly at work. Journal of Quality in Maintenance Engineering, 25(4), 612-634. https://doi.org/10.1108/jqme-05-2016-0018
46. [46] Kamsu-Foguem, B. (2016). Information structuring and risk-based inspection for the marine oil pipelines. Applied Ocean Research, 56, 132-142. https://doi.org/10.1016/j.apor.2016.01.009
47. [47] Cullum, J., Binns, J., Lonsdale, M., Abbassi, R., & Garaniya, V. (2018). Risk-Based Maintenance Scheduling with application to naval vessels and ships. Ocean Engineering, 148, 476-485. https://doi.org/10.1016/j.oceaneng.2017.11.044
48. [48] Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., Rocha-Lona, L., & Tortorella, G. (2019). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context. Journal of Manufacturing Technology Management, 30(3), 607-627. https://doi.org/10.1108/jmtm-03-2018-0071
49. [49] Tomiyama, T., & Moyen, F. (2018). Resilient architecture for cyber-physical production systems. CIRP Annals, 67(1), 161-164. https://doi.org/10.1016/j.cirp.2018.04.021
50. [50] Lattanzi, L., Raffaeli, R., Peruzzini, M., & Pellicciari, M. (2021). Digital twin for smart manufacturing: a review of concepts towards a practical industrial implementation. International Journal of Computer Integrated Manufacturing, 34(6), 567-597. https://doi.org/10.1080/0951192x.2021.1911003
51. [51] Fu, Y., Zhu, G., Zhu, M., & Xuan, F. (2022). Digital Twin for Integration of Design-Manufacturing-Maintenance: An Overview. Chinese Journal of Mechanical Engineering, 35(1). https://doi.org/10.1186/s10033-022-00760-x
52. [52] Zhang, X., & Zhu, W. (2019). Application framework of digital twin-driven product smart manufacturing system: A case study of aeroengine blade manufacturing. International Journal of Advanced Robotic Systems, 16(5). https://doi.org/10.1177/1729881419880663
53. [53] Singh, R. R., Bhatti, G., Kalel, D., Vairavasundaram, I., & Alsaif, F. (2023). Building a Digital Twin Powered Intelligent Predictive Maintenance System for Industrial AC Machines. Machines, 11(8), 796. https://doi.org/10.3390/machines11080796
54. [54] Caiza, G., & Sanz, R. (2024). Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes. Applied Sciences, 14(10), 4204. https://doi.org/10.3390/app14104204
55. [55] Abidi, M. H., Mohammed, M. K., & Alkhalefah, H. (2022). Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing. Sustainability, 14(6), 3387. https://doi.org/10.3390/su14063387
56. [56] Zhang, B., Zhang, L., & Xu, J. (2015). Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings. Quality and Reliability Engineering International, 32(2), 547-554. https://doi.org/10.1002/qre.1771
57. [57] F. Wang and T. Mamo, “Hybrid approach for remaining useful life prediction of ball bearings,” Quality and Reliability Engineering International, vol. 35, no. 7, pp. 2494–2505, 2019, doi: 10.1002/qre.2538.
58. [58] Wang, F., & Mamo, T. (2019). Hybrid approach for remaining useful life prediction of ball bearings. Quality and Reliability Engineering International, 35(7), 2494-2505. https://doi.org/10.1002/qre.2538
59. [59] Mancuso, A., Compare, M., Salo, A., & Zio, E. (2021). Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems. Reliability Engineering & System Safety, 210, 107536. https://doi.org/10.1016/j.ress.2021.107536
60. [60] El Kadiri, S., & Kiritsis, D. (2015). Ontologies in the context of product lifecycle management: state of the art literature review. International Journal of Production Research, 53(18), 5657-5668. https://doi.org/10.1080/00207543.2015.1052155
61. [61] Tsarouhas, P. (2007). Implementation of total productive maintenance in food industry: a case study. Journal of Quality in Maintenance Engineering, 13(1), 5-18. https://doi.org/10.1108/13552510710735087
62. [62] Tian Xiang, Z., & Jeng Feng, C. (2021). Implementing total productive maintenance in a manufacturing small or medium-sized enterprise. Journal of Industrial Engineering and Management, 14(2), 152. https://doi.org/10.3926/jiem.3286
63. [63] Garza-Reyes, J. A. (2015). From measuring overall equipment effectiveness (OEE) to overall resource effectiveness (ORE). Journal of Quality in Maintenance Engineering, 21(4), 506-527. https://doi.org/10.1108/jqme-03-2014-0014
64. [64] Tsarouhas, P. (2019). Improving operation of the croissant production line through overall equipment effectiveness (OEE). International Journal of Productivity and Performance Management, 68(1), 88-108. https://doi.org/10.1108/ijppm-02-2018-0060
65. [65] Wudhikarn, R. (2016). Implementation of the overall equipment cost loss (OECL) methodology for comparison with overall equipment effectiveness (OEE). Journal of Quality in Maintenance Engineering, 22(1), 81-93. https://doi.org/10.1108/jqme-12-2011-0001
66. [66] ucantoni, L., Ciarapica, F. E., & Bevilacqua, M. (2026). From predictive maintenance 4.0 to 5.0: bringing humans back into the loop with a self-learning platform and implementation roadmap on automated production lines. Journal of Quality in Maintenance Engineering, 32(5), 58-75. https://doi.org/10.1108/jqme-12-2025-0147
67. [67] Mikołajewska, E., Mikołajewski, D., Mikołajczyk, T., & Paczkowski, T. (2025). Generative AI in AI-Based Digital Twins for Fault Diagnosis for Predictive Maintenance in Industry 4.0/5.0. Applied Sciences, 15(6), 3166. https://doi.org/10.3390/app15063166
68. [68] Raza, M., Jahangir, Z., Riaz, M. B., Saeed, M. J., & Sattar, M. A. (2025). Industrial applications of large language models. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-98483-1
69. [69] Llasag Rosero, R., Silva, C., Ribeiro, B., & Santos, B. F. (2024). Label synchronization for Hybrid Federated Learning in manufacturing and predictive maintenance. Journal of Intelligent Manufacturing, 35(8), 4015-4034. https://doi.org/10.1007/s10845-023-02298-8
70. [70] Bitam, T., Yahiaoui, A., Boubiche, D. E., Martínez-Peláez, R., Toral-Cruz, H., & Velarde-Alvarado, P. (2025). Artificial Intelligence of Things for Next-Generation Predictive Maintenance. Sensors, 25(24), 7636. https://doi.org/10.3390/s25247636
71. [71] Li, T., Chen, J., Liu, T., Sun, C., Zhao, Z., Chen, X., & Yan, R. (2026). Explainable artificial intelligence based intelligent fault diagnosis: A systematic review from applications to insights. Reliability Engineering & System Safety, 267, 111935. https://doi.org/10.1016/j.ress.2025.111935