AlarmSoft: An Advanced Cloud-based Alarm Management Application
Classification
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".
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
Modern data acquisition systems allow collection and storage of huge data from process plants. The data set includes sensor readings for the process variables, alarms and events (A&E) log which contains historical alarm data for the plant as well as non-numerical linguistic records of operator interventions and actions. Process data, A&E log, operational records and piping and instrumentation diagram (P&ID) contain wealth of information about the process. Often this information remain under-utilized due to the unavailability of appropriate data mining tools. This paper proposes a comprehensive cloud-based framework that facilitates data collection from various sources into an integrated database, process and analyze data, and display the results in precise visual forms. The application has the capability to analyze huge sets of data in a computationally efficient way. There are four main functions, which are “Data Selection” to select desired processing unit, variables, time range, and parameters tuning; “Visualization” displays the results of the analysis done on process measurements and A&E log data for specified time period; “Alarm Design” allows the user to re-design an alarm configuration for a specific process variable; “Causality Analysis” discovers the relationships between process variables and shows the root cause of an event involving multiple alarms. An industrial case study has been utilized to illustrate the effectiveness and practicability of the platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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