Remaining useful life prognostics using pattern-based machine learning
Bibliographic record
Abstract
This paper presents a prognostic methodology that can be implemented in a condition-based maintenance (CBM) program. The methodology estimates the remaining useful life (RUL) of a system by using a pattern-based machine learning and knowledge discovery approach called Logical Analysis of Data (LAD). The LAD approach is based on the exploration of the monitored system's database, and the extraction of useful information which describe the physics that characterize its degradation. The diagnostic information, which is updated each time the new data is gathered, is combined with a non-parametric reliability estimation method, in order to predict the RUL of a monitored system working under different operating conditions. In this paper, the developed methodology is compared to a known CBM prognostic technique; the Cox proportional hazards model (PHM). The methodology has been tested and validated based on the Friedman statistical test. The results of the test indicate that the proposed methodology provides an accurate RUL prediction.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".