A New Method for Alarm Monitoring of Equipment Start-Up Operations with Applications to Pumps
Bibliographic record
Abstract
In practice, alarms configured for steady operating states often become a nuisance during the start-up operation of industrial equipment. As a result, they not only distract plant operators, but may also cause worse consequences, such as pump trips. Motivated by such as a practical problem, this paper presents a new alarm monitoring method for equipment start-up operations. The proposed method is capable of preventing nuisance alarms as well as achieving effective alarm monitoring during equipment start-ups. The contributions of this work are two-fold: First, an offline design framework is proposed to detect the maximum unsuppression delay time and formulate dynamic alarm limits; second, an online algorithm for alarm monitoring of equipment start-ups is proposed on the basis of the designed dynamic alarm limits and the calculation of an exact unsuppression delay time. The effectiveness and practicality of the proposed method are demonstrated by an industrial case study.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".