Physics‐informed sparse causal inference for source detection of plant‐wide oscillations
Bibliographic record
Abstract
Abstract Identification of the source of plantwide oscillations is a challenging problem, even with the availability of big data. Causality analysis is often used to construct causal maps and obtain a sequence of fault propagation for an in‐depth investigation. The reliability of the widely used Granger causality depends on the quality of the observed data. But since real‐world industrial data are prone to sensor errors, their accuracy is significantly compromised. Experienced engineers possess years of valuable process knowledge which when introduced into the modeling can significantly reduce the over‐dependence on data. In this article, we propose a novel approach to efficiently amalgamate expert information with the observed data to reconstruct causal maps. A new surrogate‐data‐based approach to test the significance of the causal relations obtained for oscillatory data is also proposed in this article. The efficiency of the proposed methodology is demonstrated using a simulation and 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 | 0.000 | 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 | 0.000 | 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".