Discovery of Alarm Correlations Based on Pattern Mining and Network Analysis
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
Alarms systems provide important alerts for the safety and efficiency of industrial facilities. However, due to complex plant connectivity and interconnections of process variables, there exist many alarms that are correlated with each other, leading to compromised alarm system performance in indicating the exact abnormality. Therefore, a systematic method to discover correlated alarms from historical Alarm & Event (A&E) logs is proposed in this work. The contributions of this study are twofold: 1) Correlated alarms are captured using a pattern mining approach, such that alarm occurrence orders are preserved to help root cause analysis; 2) network graphs are generated to visualize alarm correlations and their statistical features as indications of potential abnormality propagation paths. The effectiveness of the proposed method is demonstrated via case studies using alarm data from real industrial facilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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