Fault diagnosis of batch processes for small samples based on contrastive learning
Bibliographic record
Abstract
Abstract Fault diagnosis plays a critical role in process engineering. Existing methods often depend on large datasets for continuous process. However, for batch processes, some key variables are transient and often measured offline. Hence, the size of available datasets is small, making it difficult to effectively extract useful features for diagnosis. To overcome this limitation, this paper proposes a gated transformer network based on supervised contrastive learning (SCGTN), specifically designed for fault diagnosis in small‐sample batch processes. SCGTN incorporates a dual‐channel gated transformer network to independently extract features from the temporal dimension and multi‐variable statistics of batch process data. In this proposed framework, a supervised contrastive cost function has been incorporated as one of the loss terms into the total loss function to enhance the discriminative power of the learned representations in the feature space. The model parameters are then optimized by considering both the supervised contrastive loss and the cross‐entropy loss. Experimental results demonstrate that this method can effectively capture deep feature representations and perform reliable fault diagnosis in small‐sample scenarios. When compared to four other methods, SCGTN exhibits superior prediction accuracy and stronger generalization capabilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".