Modular fault recovery in timed discrete-event systems: application to a manufacturing cell
Bibliographic record
Abstract
This paper extends the previous results of the authors on fault recovery to timed discrete-event systems (TDES), and discusses the application of the proposed methodology to a manufacturing cell. It is assumed that the plant can be modelled as a TDES, the faults are permanent, and that a diagnosis system is available that detects and isolates faults with a bounded delay (expressed in clock ticks). Thus, the combination of the plant and the diagnosis system, as the system to be controlled, has three modes: normal, transient and recovery. Initially, the plant is in the normal mode. Once a fault occurs, the system enters the transient mode. After the fault is detected and isolated by the diagnosis system, the system enters the recovery mode. This framework does not depend on the diagnosis technique used, as long as lower and upper bounds for diagnosis delay are available. A modular switching supervisory scheme is proposed to satisfy the system specifications. The design consists of a normal-transient supervisor, and multiple recovery supervisors each for recovery from a particular failure mode. The issue of the nonblocking property of the system under supervision, and also supervisor admissibility (controllability), in particular coerciveness, are studied. The proposed approach is applied to a manufacturing cell consisting of two machines and two conveyors. A modular switching supervisor is designed to ensure the specifications in the normal mode are met. In cases of failure, the supervisor sends appropriate recovery commands so that the cell can complete its production cycle
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".