Oscillation analysis using Retrieval-Augmented Generation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".