Robust Weakly Supervised Bearing Fault Diagnosis via Dual Learning and Curriculum Strategies
Bibliographic record
Abstract
Deep transfer learning (DTL), especially domain adaptation (DA) based techniques, has broadly extended the scope of practical fault diagnosis which struggles with generalization under variable operating conditions. However, real-world applications often involve noisy source domains with significant label and feature corruption, posing severe challenges to existing DTL approaches. In this work, we introduce the weakly-supervised domain adaptation network (WSDAN), a novel framework designed to tackle these issues through transfer curriculum learning (TCL) and dual learning strategies. WSDAN prioritizes informative samples from noisy source domains and enhances cross-domain generalization by utilizing inherent supervision in unlabeled target domain data. Our extensive experiments demonstrate that WSDAN outperforms current DTL and DA methods, particularly in high noise and significant domain discrepancy scenarios. This study highlights WSDAN’s robust performance and practical utility, paving the way for more reliable fault diagnosis in complex, real-world conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".