Bibliographic record
Abstract
Abstract Increasing demands for higher efficiency and strict environmental regulations for process industries have led to the development of sophisticated control technologies and smart sensors. However, improved control mechanisms and better sensors have not been able to eliminate abnormal operating conditions. As a result operator interventions are routinely required. Alarms are at the forefront of the safety system in a plant to indicate the need for such interventions. The main purposes of an alarm is to warn of a possible critical condition and to seek the attention of an operator and thus to prevent, control and mitigate the effects of an abnormal situation. However, on many occasions, alarms have been reported as a contributor to abnormal events and the single variable based alarm system design has been identified as a main reason for that. In this article, we present a systems approach to design, analyze and prioritize alarms. By a system, we refer to a set of variables within a process. An alarm is activated based on the risk associated with the state of the variables in a system. The objectives are to integrate risk estimation with alarm design and to reduce the number of alarms. First, the process variables are grouped to be represented by a number of systems. Alarms are then assigned to each system instead of individual variables. From the measured value of the variables, the risk associated within the individual system is estimated. Also from the relationships among the variables, future risk associated with each system is evaluated. Finally, the overall risk for a particular system is obtained from the current and predicted risk and comparing the overall risk with a predefined threshold value, a decision regarding alarm activation is taken. Once a set of alarms are activated, they are prioritized based on their severity. Also for the analysis of an alarm, the risk associated with individual variables under a system is analyzed and, finally, proper operator action is suggested to mitigate the abnormal situation.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".