Bayesian-Based Method for the Remaining Useful Life and Reliability Prediction of Steel Structure
Bibliographic record
Abstract
One of the key targets of prognostic and health management is to predict the remaining useful life (RUL) and reliability of equipment. This technique can not only guarantee the reliability of the inspected component, but also reduce the maintenance cost during their lifetime. This paper proposes a RUL and reliability prediction method based on Bayesian theory and grid sampling for a steel structure. Specifically, the accumulated damage is assumed following the Paris-Erdogan model, so that Bayesian theory can be adopted to infer the unknown parameters within this model. Then a grid-sampling strategy is introduced to calculate the so-called "peak" and "profile" which are used for RUL and reliability prediction, respectively. The proposed method is adopted to the RUL and reliability prediction of a set of steel tension experimental specimens, and is benchmarked with a similar study reported recently. The result shows the superiority of this method, which can be effective even under insufficient prior knowledge.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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 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".