Cross-process alarm flood similarity analysis based on abstracted alarm descriptors
Bibliographic record
Abstract
A typical industrial facility is usually large-scale, consisting of many processes or units; some of them could be similar in functionalities or identical in architecture. Failures of the same types of equipment may lead to analogous consequential alarms, which usually have different tag names, but the same types. Therefore, if such similar alarm floods across different processes are captured, the results could help discover common root causes of alarm floods in similar processes or units, and may also give general solutions to address these alarm floods. Motivated by such a practical problem, this paper proposes a new method to identify similar alarm floods across different processes or units. This method has three main steps: 1) distill key words from detailed alarm descriptions in the alarm and event (A&E) log through word preprocessing; 2) reconstruct abstracted alarm descriptors based on key words to generalize alarm representations; 3) conduct cross-process alarm flood similarity analysis through sequence alignment. The effectiveness of the proposed method is demonstrated by an industrial case study involving real alarm data from a large-scale industrial facility.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".