A <scp>CNN</scp> approach based on correlation metrics to chemical process fault classifications with limited labelled data
Bibliographic record
Abstract
Abstract This paper proposes a novel correlation metrics‐based convolutional neural network (CNN) classification model for chemical process fault diagnoses, creating a heuristic representation concerning process variable locations in grey correlation space (GCS) in terms of the copula entropy to guide the learning of classifiers. The proposed method based on correlation metrics can help solve the problem of insufficient information caused by a lack of labelled data. Specifically, variable correlations are fused into a heuristic matrix to provide prior knowledge for network learning in compensating data information before the CNN is employed to build the classifier for mining features in GCS. Driven by this mechanism, fault classifications in the case of small numbers of fault samples are successfully implemented. With successful simulation experiments carried out on the Tennessee Eastman (TE) process platform, we found that in GCS, different fault samples can represent hugely different features, while data resulting from the same fault rarely contribute to different ones. This observation lays a solid foundation for constructing superior fault classifiers. In addition, compared with conventional approaches, the proposed method has demonstrated better fault classification performances in the case of limited labelled fault samples.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".