An abductive inference model for alarm processing [power system fault diagnosis]
Bibliographic record
Abstract
This paper introduces a system modeling concept and a new algorithm based on abductive inference for handling diagnostic problems in electrical power systems. The overall technique relies on the inherent functional and logical relationships between system elements such as lines, buses, relays and breakers to generate a diagnostic model rather than on experientially derived heuristics. This approach allows for a systematic implementation scheme that is more readily adapted to any power system. Logical models for relays, breakers, lines and buses are presented along with examples on how these models can be interconnected to represent a power system. The resulting system model can simulate the "forward" behaviour of a power system. Inputs to the forward model consist of disorders or disturbances, and the outputs are manifestations or alarms. Using abductive logic, a "reverse" system model can be generated. This system model takes the alarms as inputs and produces a diagnosis by generating sets of disorders or disturbances that explain the observed alarms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".