Reliability and safety analysis on railway signal regional computer interlocking system
Bibliographic record
Abstract
Regional computer interlocking system (RCIS) is a signal control system, which performs all of the interlocking logic operations and implements the centralized control on multiple stations using one set of interlocking equipment alone. There are two diverse RCIS solutions in China, namely, the central-ized interlocking scheme and the distributed interlocking scheme. The main defi ciency of the former lies in that the entire system would be paralyzed once the central interlocking equipment fails. The lat-ter overcomes the fl aw of the former and can disperse the danger. However, it is not suitable for some small stations due to higher upfront investment. Hence, a better selection is that the two schemes are combined together to play their respective advantages and overcome each other’s shortcomings. As a safety–critical system, the RCIS is broadly applied but the investigations on it are rarely reported in reliability and safety. Based on it, this paper establishes the Markov model of the RCIS and investigates its reliability and safety. During modeling some signifi cant factors, such as common-cause failure, cov-erage rate of diagnostic systems, online maintainability, and periodic inspection maintenance, and as well as diverse failure modes, are fully considered. The relevant researches show that the combination of the two RCIS schemes possesses better safety and reliability, and is an ideal realization mode, not only for the stations but also for the open lines between the stations.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".