Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods
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
The presence of alarm floods is identified as the main reason for low efficiency of alarm systems and the leading cause of many industrial accidents. In practice, a commonly used technique to deal with alarm floods is dynamic alarm suppression, which temporally suppresses predefined groups of alarms following unplanned events that are not relevant or meaningful to the operator. However, determining what alarms to suppress from a pool of thousands of configured alarm variables remains a challenging problem. This paper proposes a data-driven method to find such alarm groups by detecting frequent patterns in alarm floods from historical alarm data. Main contributions of this study are: 1) the identification and extraction of alarm floods are formulated; 2) frequent alarm patterns are defined and itemset mining methods are adapted to discover meaningful patterns in alarm floods; and 3) new visualization techniques are proposed based on exiting plots to show alarm floods and alarm patterns. The effectiveness of the proposed method is demonstrated by application to real industrial data.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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