Probabilistic analysis of a repairable robot‐safety system composed of (<i>n</i>−1) standby robots, a safety unit, and a switch
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 −1) standby robots, a safety unit, and a switch. Design/methodology/approach Generalized expressions for system state probabilities, system availability, reliability, and mean time to failure are developed when the failure rates of robot and safety unit are constant and the failed system repair times are arbitrarily distributed. Supplementary variable and Markov methods were used to develop these expressions. Findings This study clearly demonstrates that standby robots 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 standby robots and the repair process on the performance of the robot‐safety system. Consequently, they will make better maintenance related decisions in organizations such as automobile manufacturers that use robots quite frequently. Originality/value This paper has studied the effects of having redundant robots and the repair facilities on the performance of a robot‐safety system with perfect mechanism to turn on a standby robot. This is one of the first attempts to study the combined effects of all these factors on a robot‐safety system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".