IDENTIFICACIÓN DE FALLAS EN SISTEMAS DE BOMBEO MECÁNICO DE PETRÓLEO UTILIZANDO NEUROFUZZY
Bibliographic record
Abstract
En el bombeo mecánico de petróleo, para minimizar los costos operativos y maximizar la producción, es esencial identificar los problemas de forma rápida y precisa. El dinagrama de fondo de pozo es decisivo para analizar las condiciones de trabajo del sistema de bombeo, y normalmente el diagnóstico de fallas se ha basado en la interpretación visual de su forma, por un experto humano. Se presenta una arquitectura (NeFSuckerRod) para el diagnóstico automático del dinagrama, basada en un sistema NeuroFuzzy, que permite identificar las fallas y prescindir del experto humano. Al combinar el poder de aprendizaje de las redes neuronales artificiales y la representación explícita del conocimiento de la lógica fuzzy, y presentando un conjunto de cartas dinamométricas con diferentes fallas, el sistema NeuroFuzzy se entrena obteniendo un modelo fuzzy capaz de diagnosticar fallas en un sistema de bombeo.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".