Towards intelligent manufacturing system safety strategies: generating LockOut/TagOut sheets by Machine Learning – a case study
Bibliographic record
Abstract
Lockout/Tagout (LOTO) is a procedure that considerably reduces the risk of work-related accidents by isolating and securing the energy sources of industrial equipment. In the Industry 4.0 era, safety procedures must also evolve towards more intelligent procedures supported by technologies such as machine learning or the Internet of Things. The LOTO procedures follow sheets that indicate how to perform the security. These sheets, manually generated, could be generated automatically to optimize the LOTO procedure. This paper proposes a methodology to achieve an automatically generation of such sheets. The first sub-objective is to extract information from the available LOTO sheets provided by the industrial partner and in a second step to develop different text mining methods and word similarity search algorithms. Several options are presented to address the second sub-objective, which aims to generate the sheets from the previously obtained dataset. Finally, the future steps of this research work will be presented. In practice, an automatic generation of such sheets would enable to quickly implement the LOTO procedure for the first time on an industrial equipment, which would reduce the risk of 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".