Self-attention and subdomain adaptive adversarial network for bearing fault diagnosis under varying operation conditions
Bibliographic record
Abstract
ObjectivesDomain adaptive technology is widely used in the bearing fault diagnosis of variable operating conditions. However, most domain adaptive technology only focuses on the global domain distribution and ignores the subdomain distribution, and the domain-invariant feature quality is easily affected by noise, leading to a significant decrease in diagnostic accuracy under varying operation conditions. Therefore, a fault diagnosis method based on a self-attention subdomain adaptive adversarial network (SASAAN) is proposed.MethodsFirst, a convolutional block attention module (CBAM) is utilized to extract the fault-related domain-invariant features in the vibration signals of the source and target domains. The adversarial network and subdomain adaptive module are then combined to reduce differences in the global and local domain edge distributions of different operating condition data, thereby improving the transferability of the data. The loss function is optimized by back propagation using the Adam optimizer to improve the diagnostic performance of the model, and the hyperparameter tuning of the model is also performed. Finally，the diagnostic results on the target domain test set are output by the failure classifier, and the Ottawa bearing data set is used to validate the effectiveness of the proposed method. ,ResultsThe results show that the fault diagnosis accuracy of the proposed method is higher than 96% under the condition of strong noise and varying operation conditions, which is obviously better than other methods.ConclusionThe results of this study can provide valuable references for the fault diagnosis of rolling bearings under varying operation conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".