Fault recovery in control systems: a modular discrete-event approach *
Bibliographic record
Abstract
The problem of fault recovery is studied in discreteevent systems (DES), assuming permanent failures. A diagnosis system is assumed to be available to detect and isolate faults with a bounded delay. Thus, the combination of the plant and diagnosis system can be thought of having three modes: nonnal, transient and recovery. Initially, the plant is in the normal mode. Once a failure occurs, the system enters the transient mode. After the failure is diagnosed by the diagnosis system, the system enters the recovery mode. This framework does not depend on the diagnosis technique used, us long as lower and upper bounds for diagnosis delay are available. As a result, the diagnosis and control problems are almost decoupled. A modular switching supervisory scheme for the control problem is proposed. The design consists of a normal-transient supervisor, and multiple recovery supervisors each for recovery from a particular failure mode. The issue of nonblocking is studied and it is shown that essentially if the system under supervision is nonblocking in the normal mode, then it will remain nonblocking during the recovery procedure. Furthermore, a procedure is provided to ensure that the proposed modular switching supervisor is admissible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".