Fault diagnosis method for chemical processes based on variable correlation‐guided convolutional neural networks
Bibliographic record
Abstract
Abstract Convolutional neural networks (CNNs) have been widely applied in chemical process fault diagnosis due to their superior feature extraction capabilities. However, the inherent complexity and variability of chemical environments, involving multivariable interactions, noise interference, and other factors, pose challenges that hinder the direct application of CNNs. These limitations may compromise the accuracy of fault diagnosis by hindering the full exploitation of CNNs' feature extraction capabilities. To address these challenges, this paper proposes a novel fault diagnosis method for chemical processes based on a variable correlation‐guided CNN. The proposed method uses the Pearson correlation coefficient to identify strongly correlated variable groups, integrating them into the original variables. This integration facilitates the convolution of these strongly correlated variables, thereby enhancing the extraction of more discriminative features and optimizing fault diagnosis methods. This approach enables CNNs to more accurately extract fault‐relevant features, thereby improving diagnostic performance. The effectiveness of the proposed method is validated through comprehensive numerical simulations and the Tennessee Eastman (TE) process dataset. The results demonstrate substantial enhancements in both the accuracy and reliability of fault detection, validating the superiority of the proposed method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".