O
Orclever
Back to Journal
Research Article Open AccessOrclever Native

Analysis of OPC Data Using Federated Learning: An Evaluation of Performance and Privacy

Süleyman Burak Altınışık1,
Turgay Tugay Bilgin2
1trex Dijital Akıllı Üretim Sistemleri A.Ş.
2Bursa Technical University
Published:December 31, 2024
DOI: 10.56038/oprd.v5i1.574
Vol. 5, No. 1 · pp. 410–426

Abstract

This study examines the benefits of applying federated learning (FL) technology to OPC (Operational Performance Control) systems within industrial automation and data analysis processes. FL enables each production facility to process its data locally while only transmitting model parameters to a central server, thereby preserving data privacy. This approach provides significant advantages in industrial environments, particularly concerning data privacy and communication costs. The study evaluates FL's potential to ensure data privacy, reduce communication costs, improve efficiency in training time, and deliver high performance in predictive maintenance and quality estimation. Model performance was analyzed using accuracy, F1 score, precision, and loss metrics; the results demonstrated that FL achieved a 90% accuracy rate, offering competitive performance compared to centralized modeling. In predictive maintenance and quality analysis specifically, FL achieved 85-88% accuracy while reducing network data load by 65%. These findings validate that FL provides a secure, cost-effective, and efficient solution for industrial data analysis processes by eliminating the need for centralized data collection. In conclusion, FL and OPC integration supports data privacy, cost savings, and communication efficiency in industrial processes. The study highlights that FL could become a prevalent technology in industrial data analysis, establishing a new standard particularly in digital manufacturing processes.

Keywords
Federated Learning (FL)Operational Performance Control (OPC)Data PrivacyIndustrial AutomationPredictive Maintenance

References

  1. 1.Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and trends® in machine learning, 14(1–2), 1-210.
  2. 2.McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017, April). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR.
  3. 3.Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
  4. 4.Bonawitz, K. (2019). Towards federated learning at scale: Syste m design. arXiv preprint arXiv:1902.01046.
  5. 5.Garcia, J. M., Jeschke, S., Brecher, C., Song, H., & Rawat, D. B. (2017). Industrial Internet of Things: Challenges and Research Roadmap. In S. Jeschke, C. Brecher, H. Song, & D. B. Rawat (Eds.), Industrial Internet of Things: Cybermanufacturing Systems (pp. 70-405). Springer
  6. 6.Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems: New findings and approaches, 85-113.
  7. 7.Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing letters, 3, 18-23.
  8. 8.Xu, H., Yu, W., Griffith, D., & Golmie, N. (2018). A survey on industrial Internet of Things: A cyber-physical systems perspective. Ieee access, 6, 78238-78259.
  9. 9.Shokri, R., & Shmatikov, V. (2015, October). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security (pp. 1310-1321).
  10. 10.Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510.
  11. 11.Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., & Jirstrand, M. (2018, December). A performance evaluation of federated learning algorithms. In Proceedings of the second workshop on distributed infrastructures for deep learning (pp. 1-8).
  12. 12.Mobley, R. K. (2002). An Introduction to Predictive Maintenance. Elsevier Science google schola, 2, 485-520.
  13. 13.Sattler, F., Müller, K. R., & Samek, W. (2020). Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints. IEEE transactions on neural networks and learning systems, 32(8), 3710-3722.
  14. 14.Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., ... & Ramage, D. (2018). Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604.
  15. 15.Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557.
  16. 16.Konečný, J. (2016). Federated Learning: Strategies for Improving Communication Efficiency. arXiv preprint arXiv:1610.05492.
  17. 17.Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021). Federated learning for industrial internet of things in future industries. IEEE Wireless Communications, 28(6), 192-199.
  18. 18.Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems, 2, 429-450.
Download PDF
Cite This Article
Altınışık, S. B., Bilgin, T. T. (2024). Analysis of OPC Data Using Federated Learning: An Evaluation of Performance and Privacy. *Orclever Proceedings of Research and Development*, 5(1), 410-426. https://doi.org/10.56038/oprd.v5i1.574

Bibliographic Info

JournalOrclever Proceedings of Research and Development
Volume5
Issue1
Pages410–426
PublishedDecember 31, 2024
eISSN2980-020X