Dual‐causal analysis enhanced stacked auto‐encoders for closed‐loop industrial process monitoring
Bibliographic record
Abstract
Abstract Recently, the imperative to ensure operational safety and optimize production efficiency has inspired significant advancements in industrial process monitoring. However, due to the feedback compensation mechanism of the closed‐loop control system, the effects of incipient abnormalities are often obscured by the presence of the normal control adjustments. To tackle this challenge, we propose a dual‐causal analysis enhanced stacked autoencoders (DCSAE) network, specifically designed for closed‐loop industrial processes monitoring in dynamic. Initially, a causal self‐attention mechanism is integrated into the training process of the detection model to effectively distinguish abnormalities from normal control adjustments. Subsequently, the causality‐weighted stacked autoencoder model serves as the detection model, identifying abnormal operating conditions based on variations in reconstruction errors. To preserve the necessary causal information in the feature extraction step, conditional Granger causality analysis is incorporated into the encoder to establish the causal relationship between the operational data variables and the anomaly detection indicators. Furthermore, an adaptive threshold generator based on temporal regressor is developed to improve the accuracy of fault detection in dynamic industrial processes. Finally, the effectiveness and superiority of the proposed method are thoroughly validated through case studies involving both the vinyl acetate monomer process and a closed‐loop continuous stirred tank reactor benchmark.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".