A Structural Re-Parameterization Network for Bearing Fault Diagnosis Under Variable Working Conditions
Bibliographic record
Abstract
Abstract Deep convolutional networks are well known for their strong performance in feature characterization of massive data. Performing intelligent fault diagnosis (IFD) by training an end-to-end model could facilitate the acquisition of health state information of key machinery components while avoiding the need for human intervention and expert knowledge. However, for traditional machine learning, most methods are proposed and implemented based on the assumption that known data collected from the source domain and unknown data collected from the target domain share the same feature distribution, which can hardly be satisfied due to limitations in real industrial applications, as well as variable working conditions. To address this problem, a structural re-parameterization network is proposed, which is used to automatically extract deeper features and minimize the domain discrepancy between the source and target domain by using both metric learning and adversarial learning strategies. To be specific, an initial multi-branched network including shortcut structure is first established. This network enhances feature extraction by fusing features from multiple branches and aligning data when performing IFD under variable working conditions. After completing the training phase, the network can be re-parameterized during the inference stage, transitioning into a single-branched network while maintaining identical results, which is much more computationally efficient. Experiments have shown the effectiveness of the proposed method.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".