DTChecker: A Real-Time Signal Monitoring and Property Specification Tool for Digital Twins
Bibliographic record
Abstract
Specifying and monitoring temporal requirements in Digital Twin (DT) systems is challenging, as writing formal specifications in temporal logic is often complex and inaccessible to domain experts. This typically necessitates close collaboration with software engineers, introducing communication overhead and slowing development. We present DTChecker, a reusable selfcontained monitoring tool for DT systems built on RabbitMQbased architectures. The tool enables domain experts to write temporal specifications in a browser-based editor with language server support. These specifications are automatically translated into Signal Temporal Logic (STL) formulas and evaluated in real-time on data streams from sensors or services. Robustness scores are streamed to a front-end dashboard to visualize how well the system satisfies the specified requirements over time. This enables domain experts to write and verify temporal properties easily, thereby improving the real-time monitoring of the DT. We demonstrate the tool through integration with an open-source incubator DT case study. Video demonstration: https://youtu.be/elyhSOiGuc4?si=V2TTsXgRcYaAU2X
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".