Evaluating the use of grey-box system identification for digital twins in manufacturing automation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A key element of any digital twin is the digital replica, or model, of its physical counterpart. When applied to industrial automation systems, it is important to consider the trade-off between model fidelity and computational complexity when developing this model. In this paper, we investigate the use of grey-box system identification as a means for producing digital twin models, as the approach reduces computational complexity while maintaining model fidelity. Using a three-link robotic manipulator, we evaluate the ability the digital twin’s system identification module to produce a high-fidelity model while under the influence of input disturbances. We then expand these results by evaluating the ability of the determined model to accurately emulate the robotic manipulator for tasks given to digital twins through the system simulation and monitoring modules. The results of this testing demonstrate that the grey-box system identification module is prone to error and sensitive to input disturbances; however, it produces models that are still able to accurately predict the dynamic response of the robotic manipulator. Furthermore, the tests demonstrate the determined models can be used by the digital twin for simulation and monitoring applications with certain limitations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it