Availability analysis of a generalized maintainable three‐state device parallel system with human error and common‐cause failures
Bibliographic record
Abstract
Purpose The purpose of this paper is to study the combined effect of human error, common‐cause failure, redundancy, and maintenance policies on the performance of a system composed of three‐state devices. Design/methodology/approach Generalized expressions for time‐dependent and steady state availability of a generalized maintainable three‐state device parallel system subjected to human errors and common‐cause failures are developed in the paper under two maintenance policies: Type I repair policy (i.e. only the completely failed system is repaired); and Type II repair policy (i.e. both partially and completely failed system is repaired). The Markov method is used to develop general and special case expressions for state probabilities, and system time‐dependent and steady state availabilities. Findings In the case of three‐state devices, it is demonstrated that by increasing the number of redundant devices in parallel do not necessarily lead to the improvement in the system availability. In fact, the availability of the system depends significantly on the dominant failure mode of the devices (i.e. short‐mode or open‐mode). When comparing the effect of maintenance policies on the system availability, it is observed that the Type II repair policy does not lead to an improvement in the system availability. Furthermore, it is observed that both human error and common‐cause failure independently lead to lower system availability. Practical implications This study will help maintenance engineers and reliability practitioners to become aware of the combined impact of redundancy, human error, common‐cause failure, and maintenance policies on the performance of the three‐state device systems. Consequently, they will make better maintenance related decisions in organizations such as oil refineries and power stations that use three state devices quite extensively. Originality/value Most of the past models have independently studied the effects of redundancy, human error, and common‐cause failure on maintainable system made up of three‐state devices. This effort is one of the first attempts to study the combined effects of all these factors in a parallel system composed of three state devices.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".