Retrospective Prototype Network Based on Center Difference Measure for Cross-Machine Few-Shot Fault Diagnosis
Bibliographic record
Abstract
Metric-based meta-learning has gained extensive attention in recent years due to its rapid adaptability and strong generalization capability. However, most of the existing metric-based meta-learning methods overlook the intrinsic structures of data, and the similarity evaluation methods for the few-shot scenarios are scarce, which also need to be improved. Therefore, this article proposes a novel metric-based meta-learning method, named retrospective prototype network, for few-shot fault diagnosis across both machines and operating conditions. In this method, the retrospective prototype is developed, which utilizes the interclass variability and multidimensional correlation for accurately reflecting the complex class distributions while reducing the prototype oscillation. Moreover, considering the discrepancy between data intrinsic structures, a center difference measure is designed based on the difference between the central matrix of query sample and the prototype, thus it is more suitable for few-shot scenarios, where the high-dimensional covariance matrices are not exact and full-rank. This proposed method is successfully applied to cross-bearing few-shot fault diagnosis, and the comparative results demonstrate its superiority over the typical and advanced fault diagnosis methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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".