Diagnosability Analysis and Sensor Selection in Discrete-Event Systems with Permanent Failures
Bibliographic record
Abstract
In this paper, the problems of failure diagnosability and sensor selection for failure detection and isolation in discrete-event systems are studied. The system could operate in normal condition, or in a set of faulty conditions each corresponding to a combination of failure modes of the system. A polynomial algorithm is proposed that verifies diagnosability by examining the distinguishability of two conditions at a time. Furthermore, a polynomial procedure is presented that first finds minimal sensor sets for distinguishing one condition from another (minimal distinguishers), and then combines these sensor sets to obtain a minimal sensor set for failure detection and isolation. It is shown that taking advantage of the structure of the system, as done in the algorithms proposed in this paper, reduces the time and space complexity of testing diagnosability and sensor selection. A benefit of using minimal distinguishers is that their computation (thus, the computations for sensor selection) may be speeded up using heuristics and expert knowledge.
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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 | 0.000 |
| Open science | 0.000 | 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".