Reliability Analysis Method for Multi-State Repairable Systems Based on Goal Oriented Methodology
Bibliographic record
Abstract
This paper proposes a new GO method for repairable systems with multiple unstable operation states. First, multi-state signal flow and multi-state GO operator are defined, respectively. And the formula for calculating state probability of unit with multiple unstable operation states is deduced based on Markov theory. Furthermore, a new function GO operator named Type 19 is created to describe the unit stabilizing property. And its GO operation formulas for reliability analysis are deduced. On this basis, the reliability analysis process of multi-state repairable systems based on the new GO method is formulated. Then, this new GO method is applied in reliability analysis of Hydraulic Oil Supply System of a heavy vehicle. In order to verify the feasibility, advantage and reasonability of the new GO method, its analysis result is compared with those of FTA and the existing GO method for two-state repairable systems. All in all, this paper not only improves the theory of GO method, and widens the application of GO method, but also provides a new approach for reliability analysis of multi-state repairable systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".