Few-Shot GAN: Improving the Performance of Intelligent Fault Diagnosis in Severe Data Imbalance
Bibliographic record
Abstract
In severe data imbalance scenarios, fault samples are generally scarce, challenging the health management of industrial machinery significantly. Generative adversarial network, a promising solution to solve the data imbalance problem, suffers from a negative overfitting issue when trained with few samples. To tackle challenges, this paper proposes a Few-shot GAN which uses a sample-rich class to provide a sample distribution paradigm for the sample-poor class. More specifically, the GAN is first pre-trained using a sample-rich class. Then, a fine-tuning strategy based on anchor samples is developed, which on the one hand keeps the generated samples close to the real samples and on the other hand preserves the learned complex sample distributions as much as possible. Experiments demonstrate that the overfitting problem of the GAN with few samples trained is well solved and the diversity of the generated samples is improved. In addition, to avoid the offset of features extracted by the fault diagnosis model due to the addition of numerous generated samples in severe data imbalance scenarios, large-margin learning is introduced to constrain the similarities between the features of the generated samples and the real samples. The performance of the fault diagnosis model is significantly improved when numerous generated samples are added, benefiting the predictive maintenance-based decision and avoiding unexpected economic loss.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".