Using Augmented Reality as a tool for troubleshooting separator alarms
Bibliographic record
Abstract
Some say that the fourth industrial revolution is here now that machines can communicate with each other. This combination of Internet of Things (IoT) with industrial machines has been labeled Industry 4.0. Alfa Laval, manufacturer of liquid separators, together with B&R, an industrial automation company, want to explore the possibilities with Industry 4.0 and especially how Augmented Reality (AR) can be used as a tool when troubleshooting separator alarms. This master thesis encompasses the development and evaluation of an AR application for troubleshooting separator alarms. The final AR application shows instructions to the user and highlights the corresponding components as 3D models in AR. User tests showed that the application can be useful for people without experience of troubleshooting separator alarms. Additionally, the tool can indirectly reduce the workload for expert service technicians since they don’t have to show up and troubleshoot the simpler alarms. In the future, a tool similar to this can be used during the training process of prospective service technicians.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".