Oscillation analysis using Retrieval-Augmented Generation

Industrial control system oscillations pose significant operational challenges, causing economic losses through energy waste, equipment degradation, and reduced product quality. Traditional detection methods rely heavily on manual expert analysis, creating scalability constraints in facilities with thousands of control loops. This paper presents a novel framework integrating Large Language Models (LLMs) with specialized oscillation detection toolboxes through Retrieval-Augmented Generation (RAG). The system features a command-line interface enabling seamless programmatic interaction between LLMs and analytical tools, eliminating GUI dependencies. A domain-specific RAG architecture combines real-time analytical outputs with technical knowledge repositories, while natural language processing capabilities allow industrial personnel to query systems using everyday language. The framework incorporates triangle-like shape detection algorithms enhanced by intelligent LLM interpretation. Validation using industrial datasets from refinery operations, the International Stiction Database, and the Tennessee Eastman Process benchmark demonstrates substantial performance improvements, achieving excellent classification accuracy and correlation values exceeding 0.96, effectively democratizing access to advanced oscillation analysis capabilities.