Automatic parameter update algorithm for regression models to estimate remaining useful life: Application in an offshore natural gas dehydration unit
Bibliographic record
Abstract
Abstract Remaining useful life (RUL) analysis of equipment prevents failures and assists in maintenance to address impending failures. To achieve this, the advancement of degradation can be measured through a determinant variable. This work presents a new methodology for estimating the number of remaining cycles (NRC), or RUL, of equipment in cyclic processes, consisting of an algorithm that allows for the fit of linear and nonlinear models to predict RUL/NRC in a natural gas dehydration unit. The study compares the results of the proposed algorithm with a Bayesian approach. It highlights the efficiency of the logistic function in estimating NRC, especially in fixed beds with a similar or higher lifetime than the reference fixed bed. Conversely, the Bayesian methodology yielded better results in fixed beds with shorter lifetimes than the reference fixed bed. The proposed algorithm was successfully integrated into a real‐time monitoring dashboard of a Brazilian oil and gas plant to predict the RUL of fixed beds in an offshore natural gas dehydration unit.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| 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".