Signed directed graph‐based hierarchical modelling and fault propagation analysis for large‐scale systems
Bibliographic record
Abstract
The signed directed graph (SDG) model can be considered as a qualitative model to describe the variables and their cause–effect relations in a continuous process. Such models can allow one to obtain the fault propagation paths using the method of graph search. In this way, the authors can use SDGs to model and analyse the propagation of faults in large‐scale industrial systems. However, with increasing system scales, the time requirements of a graph search method would be too onerous. This can be alleviated by transforming a single‐layer SDG model into a hierarchical model to improve search efficiency. The hierarchical model would be composed of three layers: the top layer would consist of independent sub‐systems; the middle layer would have control systems configuration and the bottom layer would have all the variables. The possible root causes of faults can then be searched in this model, layer by layer according to the initial response of the system. The efficacy of the proposed approach is illustrated by application to a four‐tank system and a generator system in a power plant. The methodology presented here can also be used in process hazard analysis.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".