@misc{10481/107588,
year = {2025},
month = {9},
url = {https://hdl.handle.net/10481/107588},
abstract = {This systematic literature review examines the integration of Machine Learning techniques
within industrial system architectures using OPC-UA for process automation. Through
analyzing primary studies published between 2018 and 2024, the review identifies key
trends, methodologies, and implementations across various industrial applications. The
review identifies a marked increase in research focused on hybrid architectures that integrate Machine Learning with OPC-UA, particularly in applications such as predictive
maintenance and quality control. However, despite reported high accuracy rates—often
above 95%—in controlled environments, there is limited evidence on the robustness of
these solutions in real-world, large-scale deployments. This highlights the need for further
empirical validation and benchmarking in diverse industrial contexts. Implementation
patterns range from cloud-based deployments to edge computing solutions, with OPC-UA
serving as a communication protocol, information modeling framework, and specifically
using the finite state machine specification. The review also highlights current challenges
and opportunities, providing valuable insights for researchers and practitioners working
on intelligent industrial automation.},
organization = {University of Granada - ESPOL (Polytechnic University)},
publisher = {MDPI},
keywords = {OPC-UA},
keywords = {Industry 4.0},
keywords = {Control systems},
title = {Industrial Process Automation Through Machine Learning and OPC-UA: A Systematic Literature Review},
doi = {10.3390/electronics14183749},
author = {Velesaca, Henry Oswaldo and Holgado Terriza, Juan Antonio and Gutiérrez Guerrero, José Miguel},
}