OPC-UA in artificial intelligence: a systematic review of the integration of data mining and NLP in industrial processes
Abstract
This systematic literature review explores the integration of OPC-UA with Data Mining and Natural Language Processing (NLP) techniques within industrial environments. As industrial automation evolves, this integration faces challenges related to intelligence, autonomy, security, privacy, and interoperability—similar. The review evaluates current methodologies and applications aimed at addressing these challenges, particularly in areas like predictive maintenance, anomaly detection, process optimization, and others. Reviewing several primary studies, selected from high-impact scientific databases this paper identifies key strengths, weaknesses, opportunities, and threats in leveraging OPC-UA protocols for AI-based automation. Moreover, it highlights trends and future directions for improving decision-making processes and enhancing machine interoperability in data-driven industry.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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