Root cause diagnosis with co‐integration constrained Liang–Kleeman information flow in non‐stationary processes
Bibliographic record
Abstract
Abstract To cope with the challenge of non‐stationary characteristics to causal identification, this paper proposes a Liang–Kleeman information flow framework under co‐integration residual constrained for root cause diagnosis in industrial processes. Specifically, the stationarity is first determined by combining augmented Dickey–Fuller and Kwiatkowski–Phillips–Schmidt–Shin tests, and the non‐stationary variables can be screened out. Then, the co‐integration analysis is used to identify the long‐term equilibrium relationship between variables, and the driving term is constructed with co‐integration residuals. Next, the residual matrix is embedded in the Liang–Kleeman information flow to improve the ability to characterize the changes in causal intensity in non‐stationary processes. Finally, experiments are carried out based on typical industrial process Tennessee Eastman Process to verify the effectiveness and applicability of the proposed method in root cause diagnosis.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".