Fault diagnosis in hierarchical discrete-event systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it