Safety Assessment for Industrial Robots
Bibliographic record
Abstract
The safety and reliability of robots, like other engineering products, have been considered as important issues in many countries since the growing robot technology entered the industry. An industrial robot must be safe and reliable so that it does not lead to unsafe situations and high maintenance expenses. The growing application of industrial robots in some of the industries of Iran, and the nature of their activities (vast work environment, unpredictable movements, and the nature of controlling its computer program) will create a unique challenge in occupational safety. Consecutive failures of a robot will cause an industry to suffer from great expenses. This study seeks to develop a safety analysis model for industrial robots. Due to the importance of this issue and the dearth of studies done in this regard, this study intended to develop a safety analysis model for industrial robots based on Markov chain. Then, this model was applied to the robots in Haierplast Company; and finally, the results were analyzed. The findings of this study include the computation of danger rate, probabilities, reliability, the average of failure time, and the repair rate of the safety system of robots. Keywords: robot safety, reliability, severity-frequency index of an event
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".