Methodology and Application of Pattern Mining in Multiple Alarm Flood Sequences∗∗This work was supported by an NSERC CRD project.
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
Alarm floods have always been serious hazards in industrial process monitoring since they overwhelm operators with large amount of alarm messages raised within a short period of time. In this paper, we propose an algorithm to find an optimal alignment of multiple alarm flood sequences so that based on this alignment an alarm sequence pattern can be easily found. The pattern could reveal the correlation between alarm messages in the alarm floods, which cannot be obtained by applying other alarm management techniques such as delay timers and dead-bands. The pattern could also help find the root cause, locate badly designed part in alarm systems, and predict incoming alarm floods. A dataset from an actual chemical plant has been used to test the effectiveness of the proposed algorithm.
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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 | 0.000 |
| 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 it