Fault-tolerant supervisory control with permanent faults
Bibliographic record
Abstract
In our earlier work, we introduced a discrete-event system-based fault-tolerance approach designed to handle intermittent faults. This approach is different from the typical fault-tolerant methodology as the approach does not rely on detecting faults and switching to a new supervisor; it requires a supervisor to be designed that works correctly under normal and fault conditions. This is a passive approach that relies upon inherent redundancy in the system being controlled. This is also a foundation method that should allow a wide variety of existing fault approaches to be modelled but still allow controllability and nonblocking properties to be verified. Permanent faults could be modelled in this framework, but the current method was onerous. In this paper, we introduce a new modelling approach for permanent faults that is easy to use, as well as a set of new permanent fault-tolerant definitions. They are designed to capture several types of permanent fault scenarios (generic situations such as at most one fault occurs) and to ensure that our system remains controllable and nonblocking in each scenario. New definitions and scenarios were required as the previous ones were incompatible with the new permanent fault modelling approach. Finally, we present algorithms to verify these properties, followed by complexity analyses and correctness proofs of the algorithms. An example is then provided to illustrate our approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".