Dual‐noise autoencoder combining pseudo‐labels and consistency regularization for process fault classification
Bibliographic record
Abstract
Abstract In the classification of industrial process faults, the collected process fault data has the problem of having more irrelevant fault information, limited labels, and a significant impact of noise, which affects the prediction accuracy of the classification model. To address these problems, this paper proposes a semi‐supervised dual‐noise autoencoder method that integrates pseudo‐labels and consistency regularization (PR‐SNAE). Based on normal samples, the differences between faulty samples and normal samples are enhanced through dissimilarity analysis. Two types of noise are introduced into the enhanced samples to improve the robustness of the model. A stacked supervised autoencoder (SSAE) network is trained using a small amount of labelled data. The deep feature information is extracted to establish a preliminary fault classification model. Pseudo‐labels are generated for unlabelled samples to overcome the problem of insufficient labels for fault data. In the adjustment stage of the classification model, a loss function that integrates pseudo‐labels and consistency regularization is proposed to prevent overfitting and poor robustness of the model. Simulation experiments were conducted on the Tennessee Eastman (TE) benchmark process and three‐phase flow process, and the results verified the effectiveness 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".