Fault diagnosis in hierarchical discrete-event systems
Bibliographic record
Abstract
A framework for on-line passive fault diagnosis in hierarchical discrete-event systems (DES) is proposed. In this approach, the system model is broken into simpler substructures called D-holons. A state-based diagnoser is constructed for each D-holon. Fault diagnosis is accomplished using the state estimates provided by the D-holon diagnosers. At any given time, only a subset of the diagnosers are active, and as a result, instead of the entire model of the system, only the models of the D-holons associated with the active diagnosers are used. This reduces random access memory (RAM) requirements and thus, could be useful in complex multi-phase systems. Based on the D-holon model, the concept of phase-diagnosability is introduced to study failure diagnosability in cases where each component may be active only in some of the phases of operation. The computational complexity of constructing the transition systems required for diagnosis is exponential in the number of components. To reduce the computational complexity, we propose a semimodular approach with polynomial complexity for cases where interactions among system components are observable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".