Probabilistic Analysis for Remaining Useful Life Prediction and Reliability Assessment
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
Although the importance of remaining useful life (RUL) prediction is widely recognized in industries, its implementation in real scenarios is highly restricted by the complexity of the degradation mechanism, uncertainty of machinery, and insufficiency of prior knowledge. To address such a challenge, this article proposes a model-based framework, which has the capability to integrate multiple predictive models via a probabilistic mechanism. When a new observation is fed into each predictive model, the posterior distribution of each model will be updated via Bayesian inference. Then, a grid-sampling strategy is applied to their posterior distributions for identifying the “peak” and “profile,” which are used for RUL prediction and reliability assessment, respectively. The effectiveness of this framework is validated with the experiments on a set of steel tension specimens. Theoretical interpretations and comparative studies demonstrate the superiority of the proposed framework. Besides, the proposed framework can not only reduce human workload on trivial parameter setting but also be effective with insufficient prior knowledge, making the intelligent RUL prediction easier.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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