A Diagnosis-Based Siamese Network for Fault Detection Through Transfer Learning
Bibliographic record
Abstract
Traditional deep-learning-based approaches often struggle with data imbalance and variability across fault conditions and normal scenarios, especially in industrial processes. Besides, inconsistent feature distributions from combining different fault conditions into the same category are a limitation for many data-driven algorithms. This study proposes a fault detection framework that combines Siamese neural networks with transfer learning, using a pretrained fault diagnosis model as its backbone, taking advantage of knowledge related to the attribute space that characterizes individual fault patterns. Our method transforms the detection classification problem into an embedding similarity task, allowing for improved differentiation between normal and faulty operations. This approach poses an alternative for data imbalance and a lack of labeled anomaly data, as it is based on the combination of normal and faulty time series. Our best model achieved an F1-score of 91.41% on the test set, and the t-distributed stochastic neighbor embedding indicates that the knowledge transferred from diagnosis allowed the detection model to generate embeddings that discriminate between most faulty conditions. When analyzing individual fault detection rates, we observed that our model demonstrated superior performance compared with recent literature for most fault cases.
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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 | 0.001 |
| 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) | 0.002 | 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".