A Practical Approach to Industrial Digitalization through Data Acquisition and Systems Integration for Predictive Maintenance

Nikita Bocharov; Pedro Torres; João Matos

Authors

Nikita Bocharov Polytechnic Institute of Castelo Branco
Pedro Torres Polytechnic Institute of Castelo Branco https://orcid.org/0000-0003-4835-5022
João Matos The Navigator (Portugal)

Keywords:

Industry 4.0., OPC UA, Legacy Equipment Integration, Industrial Digitalization

Abstract

The digital transformation of industrial environments requires the ability to collect, process, and integrate data from production systems in real time. However, many manufacturing facilities operate with legacy equipment that is perfectly functional and operational but lacks native connectivity or standardized interfaces for data acquisition. This paper presents an approach to enable industrial digitalization through the implementation of a network architecture at the Operational Technology (OT) level that facilitates the collection of structured data from legacy and modern machines. The proposed solution ensures integration between production systems and supervisory platforms, supporting real-time monitoring through SCADA systems and providing relevant information for predictive maintenance strategies. The proposal is based on the implementation of a standardized and secure communication infrastructure between the shop floor and higher-level Information Technology (IT) systems, aligning with the principles of Industry 4.0. The solution has been implemented in a real industrial scenario, is fully operational and the results demonstrate significant benefits of integrating heterogeneous industrial assets into a unified data ecosystem, improving process insight, operational efficiency and supporting maintenance decision-making.

References

[1] Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806.

[2] Liao, Y., Deschamps, F., Loures, E. de F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629. https://doi.org/10.1080/00207543.2017.1308576.

[3] Kang, H.S., Lee, J.Y., Choi, S. et al. (2016). Smart manufacturing: Past research, present findings, and future directions. Int. J. of Precis. Eng. and Manuf.-Green Tech. 3, 111–128. https://doi.org/10.1007/s40684-016-0015-5.

[4] Mahnke W, Leitner SH, Damm M. OPC Unified Architecture. Berlin: Springer; 2009. 332 p.

[5] Horak, T., Strelec, P., Kebisek, M., Tanuska, P., & Vaclavova, A. (2022). Data Integration from Heterogeneous Control Levels for the Purposes of Analysis within Industry 4.0 Concept. Sensors, 22(24), 9860. https://doi.org/10.3390/s22249860.

[6] European Commission (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Luxembourg: Publications Office of the European Union.

[7] Korodi, A., Nițulescu, I.-V., Fülöp, A.-A., Vesa, V.-C., Demian, P., Braneci, R.-A., & Popescu, D. (2024). Integration of Legacy Industrial Equipment in a Building-Management System Industry 5.0 Scenario. Electronics, 13(16), 3229. https://doi.org/10.3390/electronics13163229.

[8] Ladegourdie, M., & Kua, J. (2022). Performance Analysis of OPC UA for Industrial Interoperability towards Industry 4.0. IoT, 3(4), 507-525. https://doi.org/10.3390/iot3040027.

[9] Hirsch, E., Hoher, S., & Huber, S. (2023). An OPC UA-based industrial Big Data architecture. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2306.01418.

[10] Torres, P. M. B., Spencer, G., Lopes, P., & Santos, F. (2024). Industrial IoT platforms enabling industry 4.0 digitization towards industry 5.0. In Lecture notes in mechanical engineering (pp. 1–12). https://doi.org/10.1007/978-3-031-61575-7_1.

[11] R. H, S. J. Fusic, J. Cornelius K S R, S. M and A. M, (2023). Case study on Integrating Legacy Devices in Industry 4.0 framework using OPC UA, 2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE), Madurai, India, pp. 1-6, doi:10.1109/ICEMCE57940.2023.10434133.

[12] S. Yolov, G. Hristov and P. Zahariev (2025), Development and Integration of an Industrial Automation System based on the MQTT Protocol, 2025 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Nan, Thailand, pp. 79-84, doi:10.1109/ECTIDAMTNCON64748.2025.10962086.

[13] A. Vicentin and F. Lima (2024), Retrofitting Legacy CNC Machines with a Focus on Energy Consumption," 2024 International Conference on Sustainable Technology and Engineering (i-COSTE), Perth, Australia, pp. 1-6, doi: 10.1109/i-COSTE63786.2024.11025111.

[14] Patel, D., & Muthuswamy, S. (2024). An open framework for smart retrofitting of legacy industrial machines towards industry 4.0. TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), 971–974. https://doi.org/10.1109/tencon61640.2024.10902873.

[15] OPC UA, OPC Foundation, https://opcfoundation.org/, accessed on July 2025.

[16] vNODE, https://vnodeautomation.com/, accessed on July 2025.

[17] Kepware, https://www.ptc.com/en/products/kepware, accessed on July 2025.

[18] Ignition, https://inductiveautomation.com/ignition/, accessed on July 2025.