Relationship and Changing Analysis of Birnbaum Importance for Different Components with Bayesian Networks
Bibliographic record
Abstract
Importance measures are widely used in reliability engineering. To support system reliability optimization, this paper studies the relationship and changing characteristics of the Birnbaum importance measures (BMs) for different components in binary coherent systems. First, the probabilistic meaning of BM and a modeling method for binary coherent system based on Bayesian network (BN) are presented. Then, the relationship of BMs for different components is explored according to the position of corresponding nodes (components) in BN structures. Later, the changing of BMs for different components, which is caused by the reliability improvements of related root or middle nodes (components) in BN structures, is analyzed respectively. Finally, an illustrative example of a helicopter convertor is implemented to demonstrate the calculation and application process of the relationship and changing characteristics in binary coherent systems with BN. The experimental analysis results substantiate the correctness and effectiveness of the proposed relationship and changing characteristics of BMs for different components.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".