Generative-Diffusion-Model-Based Deep-Learning Framework for Remaining Useful Life Prediction
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
In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: 1) a feature extractor corresponding to the encoder part of our customized U-Net and 2) a RUL predictor constructed by a multilayer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pretrained for high-quality feature learning, and then, is retrained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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