Reliability evaluation of standby safety systems due to independent and common cause failures
Bibliographic record
Abstract
Standby redundant systems are often adopted in critical applications such as the emergency shutdown systems (ESDS) in nuclear power plants (NPPs). One failure mode of the standby redundant systems is that they are not available when there is a demand. This is a serious safety issue. Another failure mode of the standby safety critical systems is that they function spuriously when there is actually no need. Once this occurs, the normal plant operation will be interrupted; certain equipment could be damaged; and restarting the plant could be very costly. The objective of this paper is to evaluate the unavailability and the probability of spurious operation of k-out-of-n systems when they are subjected to both independent and common cause failures (CCFs). A load-strength interference model is adopted for CCF analysis. A data mapping technique is utilized when there is no data available for a specific system. It is concluded quantitatively that the k-out-of-n system has a lower unavailability but a higher probability of spurious operation than the k-out-of-(n-1) system, under both independent failure and CCFs. This result complies with common sense and practical experience. The two different configurations adopted in different types of NPPs, the 2-out-of-3 system and the 2-out-of-4 system, are used to demonstrate the theoretical analyses that are developed in this paper. However, due to the lack of relevant data, the analysis of probability of spurious operation under CCFs are only explained in a qualitative manner
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".