Failure prediction in the refinery piping system using machine learning algorithms: classification and comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pipelines play a pivotal role in transporting large volumes of oil and gas within refineries. However, over time, they are susceptible to deterioration, leading to potential failures. Effective monitoring is imperative to maintain their optimal performance and safety. This research introduces a machine learning (ML) approach to pinpoint failure sources in oil and gas pipelines. Analysing an industrial dataset, we compared six ML models to predict failures in refinery pipelines. Leakage sources are predicted based on three operational parameters: transported fluid, temperature, and pressure. The models are evaluated and compared in terms of precision, recall, F1-score, accuracy, and the ROC-AUC. Remarkably, the XGBoost classifier exhibited a 99.7% accuracy, outperforming other algorithms in predicting the failure source. Emphasizing the value of Industry 4.0 solutions, this study underscores the potential of advanced ML in enhancing pipeline monitoring. Such predictions empower operators to pre-empt failures, reinforcing industry safety and sustainability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it