Data-Driven Remaining Useful Life Prediction to Plan Operations Shutdown and Maintenance of an Industrial Plant
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
Predicting remaining useful life (RUL) of critical machines in heavy maintenance industries, such as oil refineries and upgrading, is crucial to deliver robust and cost-effective plans for resource procurement and maintenance scheduling in a challenging and constrained work environment. Statistical methods have been extensively utilized based on condition monitoring (CM) and sensor data. In this paper, we investigate the use of statistical methods in RUL prediction and conduct the taxonomy of those methods to provide better understanding of their potential application in the context of asset maintenance management for an industrial plant. Then the application of statistical learning methods for data-driven RUL prediction is illustrated with sensor data collected from a running plant. The research deliverable is intended to provide data-driven RUL predictions as part of plant asset management system and shed light on decisions directly pertaining to industrial plant maintenance management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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