Reliability Prediction Approach Based on Non-Probabilistic Interval Analysis: Case Study of Transmission System
Bibliographic record
Abstract
This paper presents a reliability prediction approach based on non-probabilistic interval analysis. First, the interval reliability prediction model is presented from the aspects of interval stress-strength interference model, and interval reliability criteria. Then, the process of reliability prediction approach based on non-probabilistic interval analysis is formulated. Furthermore, the reliabilities of Planetary Gear Drive Mechanism at different confidence coefficients are obtained by using the non-probabilistic interval analysis. Finally, the result of the non-probabilistic interval analysis is compared with the results of non-probabilistic convex model and the probabilistic method in order to illustrate the characteristics, advantage and engineering practicality of the non-probabilistic interval analysis. In addition, the consistent relationship between reliability by using probabilistic method and the reliability by using non-probabilistic method is preliminarily discussed. All in all, this paper not only takes planetary gear drive mechanism as a case to provide guidance for mechanisms by using the reliability prediction approach based on non-probabilistic interval analysis, but also it points out the direction for future research of the reliability prediction approach based on non-probabilistic interval analysis.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".