Unsupervised Cross-Domain Fault Diagnosis Using Feature Representation Alignment Networks for Rotating Machinery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, the problem of the cross-domain fault diagnosis of rotating machinery is considered. In a practical setting of this approach, the operating platform of the machine may have a different setup and conditions compared to the experimental platform that is used to collect the training data. This can lead to significant data variations, specifically domain shifts. Conventional data-driven approaches are known to adapt poorly to these domain shifts, resulting in a significant drop in the diagnosis accuracy when the pretrained model is applied in the actual operating situation. In this article, an unsupervised domain adaptation approach is developed to mitigate the domain shifts between the data gathered from the experimental platform (the source domain) and the operating platform (the target domain) by aligning the features extracted from the two data domains. The mutual information between the target feature space and the entire feature space is maximized to improve the knowledge transferability of the labeled data in the source domain. Furthermore, the feature-level discrepancy between the two domains is minimized to further improve diagnosis accuracy. The experiments using public datasets and real-world adaptation scenarios demonstrate the feasibility and the superior performance of the proposed method.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it