Reliability and availability analysis of a robot‐safety system
Bibliographic record
Abstract
Purpose The purpose of this paper is to study reliability, availability, and mean time to failure of a repairable robot‐safety system composed of n robots, m safety units, and a perfect switch. Design/methodology/approach Generalized expressions for system state probabilities, system availability, reliability, and mean time to failure are developed. Supplementary variable and Markov methods were used to develop these expressions. Findings This study clearly demonstrates that backup robots, safety units, and the repair process help to improve system availability. Practical implications This study will help maintenance engineers and reliability practitioners to become aware of the combined effect of backup robots, safety units, and the repair process on the performance of the robot‐safety system. Consequently, they will make better maintenance related decisions in organizations that make use of robots. Originality/value This paper has studied the effects of having redundant robots, safety units, and the repair facilities on the performance of a robot‐safety system with perfect mechanism to turn on a safety unit. This is one of the first attempts to study the combined effects of all these factors on a robot‐safety system.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".