A new reliability analysis method for vehicle systems based on goal-oriented methodology
Bibliographic record
Abstract
This paper presents a new approach for the reliability analysis of vehicle systems by considering typical characteristics based on goal-oriented methodology. First, solutions are proposed for vehicle systems with multiple fault modes, a standby structure at any position, a multiple-input closed-loop feedback link, multiple functions, and multiple conditions for the goal-oriented method. Then, a two-level goal-oriented model and the new rules of goal-oriented operation for such vehicle systems are proposed. Furthermore, the quantitative method and the qualitative method are improved. In addition, the analysis process for these vehicle systems based on the new goal-oriented method is formulated. Finally, this new goal-oriented methodology is applied in the dynamic availability analysis and qualitative analysis of the power-shift steering transmission for a heavy military vehicle. In order to verify the feasibility, the advantages, and the correctness of the new goal-oriented methodology, the results are compared with those from fault tree analysis and Monte Carlo simulations. In general, this study not only improves the theory of the goal-oriented method and widens the application of the goal-oriented method but also provides a new reliability analysis method for such vehicle systems. In addition, the analysis process of the new goal-oriented method shows that the goal-oriented method has advantages in system reliability modeling and system reliability analysis for vehicle systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".