A Robust Discrete Event Method for the Design of Cyber-Physical Systems
Bibliographic record
Abstract
The technological advancement in Cyber-Physical Systems (CPS) has seen more sophisticated hardware, leading to systems that are complex, interconnected, and require automation.This trend has made modern CPS fragile and susceptible to faults.Traditional methods for fault detection and diagnosis are unable to adequately scale up to handle the faults that occur in CPS because of the tight interconnectivity between the physical and cyber parts of CPS.Also, real-time requirements present new challenges that are not sufficiently addressed by traditional fault-tolerant design approaches, therefore more intelligent methods are now needed to deal with these faults.To address these issues, we propose an approach to formally define faults, detect, and diagnose faults, and accommodate faults for fault tolerance.For fault detection and diagnosis, we propose a generic fault detection and diagnosis (FDD) scheme capable of diagnosing CPS faults in real-time.The scheme is developed to accommodate different modeling methods but for clarity of explanation, we adapt it to the DEVS formalism.To test the scheme, we implemented a library to store fault codes in a data structure and developed intelligent logic to ensure faults are correctly detected and isolated.We also propose a purely data-driven approach to detect faults in CPS where we have no control over the design of the control system.Our data-driven methods thrive on setting rigorous processes and workflows to ensure that representative data is collected for CPS to ensure that FDD techniques perform optimally.iii For fault tolerance, a fault-tolerant logic is included as part of the FDD scheme for the control system, and for sensing, we propose a sensor fusion framework developed not just to facilitate the implementation of sensor fusion algorithms in DEVS.It also provides a method for checking failures using data obtained from sensor outputs.This improves the reliability of the sensing system.Finally, we evaluate the applicability of the various methods proposed through case studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".