Testing and Improving an ISO 14119-Inspired Tool to Prevent Bypassing Safeguards on Industrial Machines
Bibliographic record
Abstract
Various safety-related standards associated with the machinery design phase, such as ISO 14119:2013, emphasize the appropriate design and selection of protective devices to prevent bypassing. Despite such standards, bypassing safeguards is a common issue at the machinery use phase. ISO 12100:2010 indicates, “experience has shown that even well-designed safeguarding can fail or be violated”. This unsafe practice can cause serious injuries or fatalities. This paper presents an improved version of a bypassing-related assessment tool initially inspired by ISO 14119. The improvement results from testing its performance through industrial case studies to explore how the tool works in reality. Five occupational health and safety (OHS) practitioners apply this tool in four plants in Quebec to 18 machines and 37 activities. Afterwards, the OHS practitioners provide feedback using a questionnaire. The findings reveal that the tool is appropriate for the machine usage phase to prevent bypassing with an overall 82% satisfaction score. The probability levels of bypassing given by the tool enable a safety improvement prioritization method for the machines and safeguards. The tool was improved, redefining some incentives to bypass and its layout. The findings explain how practitioners could influence decision-making to minimize incentives to bypass and the probability of bypassing to prevent accidents.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".