Open Set Online Classification of Industrial Alarm Floods with Alarm Ranking
Bibliographic record
Abstract
Alarm floods can cause serious safety problems in complex industrial plants by overwhelming a plant operator with many alarm annunciations in a short time interval. In a plant operation, there can be alarm flood scenarios that correspond to previously unseen abnormal situations. Therefore, early online assistance for plant operators in both previously known and new situations is of great importance. The aim of this article is to develop an operator assistance system based on early classification of alarm floods and alarm ranking. A weighting method is developed to model alarm flood sequences as feature vectors while preserving key characteristics of them, including the temporal information of alarms. The proposed weighting strategy is defined by considering early classification accuracy and can also provide ranking of alarms according to their relevance to the abnormal situation. To handle the new alarm flood scenarios, an open set classification method based on a systematic similarity threshold estimation is proposed. The effectiveness of the proposed approach is evaluated by using the Tennessee Eastman benchmark.
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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 | 0.000 |
| 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 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".