Reliability analysis of multi-state systems with s-dependent components
Bibliographic record
Abstract
The assumption of stochastic independence between components is frequently made in studies of system reliability. However, in a specific system, the failure of a component can trigger the failure of other components, or the current health condition of a component may affect the performance of other components. Thus, the state of a component can affect the state and degradation of other components in a multi-component system, that is, stochastic dependence (s-dependence) may exist in real and complex systems. In this paper, reliability analysis of multi-state systems (MSS) with s-dependent components will be considered. The MSS consists of 2 multi-state components in series, with each component possibly operating at different performance levels, varying from perfect functioning to complete failure. When the first component degrades to a lower performance level, it affects the state as well as the degradation of the other component in the system. A combined technique of stochastic process and modified universal generating function is used to evaluate the system reliability. The combined approach is then verified using the Monte Carlo Simulation method. An illustrative example on reliability analysis of MSS with dependent components is also provided.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".