Modeling of Fault Recovery and Repair for Automated Manufacturing Cells with Load-Sharing Redundant Elements Using Petri Nets
Bibliographic record
Abstract
Failure of resource in automated manufacturing systems could cause a complete system shutdown. This paper addresses the issue of unreliable resource failure in manufacturing cells through the use of load-sharing redundant resources (LSRRs). The aim is to use more than one type of a failure-prone resource to share tasks between a failure-prone resource, called a target resource, and reliable ones called load-sharing redundant resources (LSRRs). Both an unreliable resource and its LSRR perform the same tasks, and there is normally a system that assigns tasks to them. If the target resource fails, all the tasks will be performed by the LSRRs. After the faulty target resource is fixed and restored, its assigned tasks are automatically returned to it. This way the system can continue to produce or process parts. Thus, a total system shutdown due to unreliable resource failures is eliminated. The proposed method is tested using real examples. The results, compared with those obtained by the studies in the literature, show that the proposed method has an outstanding performance and outperforms some of the existing studies.
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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 | 0.001 |
| 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".