Fault Recovery in Discrete-Event Systems using Observer-Based Supervisors
Bibliographic record
Abstract
We solve the supervisory design problem using a state-based approach. It is assumed that design specifications are given for normal, transient and recovery modes in terms of legal (safe) states. The system under supervision is also required to be nonblocking in normal and recovery modes. Following a modular switching approach, we propose supervisory schemes in which separate supervisor modules are designed for normal, transient and recovery modes. We consider failure accommodation in cases where recovery to normal operation is not possible and also recovery in cases in which it is possible to resume normal operation. For each case, we provide two solutions, one in which the recovery supervisor is in the feedback loop when the system is started in its normal mode, and another solution in which the recovery supervisor is engaged only when a fault is detected and isolated. The latter approach is less computationally complex to implement. We investigate supervisor admissibility and nonblocking property of the system under supervision. All of the supervisor modules are observer-based. In our opinion, the use of observer-based supervisors results in a more transparent solution and simplifies the analysis in our switching scheme when one supervisor replaces another in the feedback loop. In this thesis, in the process of our study of fault recovery, we also propose a systematic method for designing observer-based supervisors using normal languages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".