Methods for root cause diagnosis of plant‐wide oscillations
Bibliographic record
Abstract
Plant‐wide oscillations are common in many industrial processes. They may impact the overall process performance and reduce profitability. It is important to detect and diagnose such oscillations. This paper reviews advances in diagnosis of plant‐wide oscillations. The main focus of this study is on identifying possible root causes of oscillations using two techniques, one based on data analysis in the temporal and spectral domains and the other based on process connectivity analysis. The process data‐based analysis provides an effective way to capture the difference between the root cause variable and the secondary propagated oscillating variables. It is shown that process topology‐based methods are capable of finding oscillation propagation pathways and, thus, help in determining the root cause. This paper discusses and compares five such methods—spectral envelope, adjacency matrix, Granger causality, transfer entropy, and Bayesian network inference methods— by application to an industrial benchmark dataset. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2019–2034, 2014
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".