Multi-scale Adaptive Fusion for Rolling Bearing Fault Diagnosis
Bibliographic record
Abstract
In order to fully extract the bearing fault feature information under strong noise and variable load, a rolling bearing fault diagnosis method based on multi-scale adaptive fusion (MSAF) is proposed. Firstly, a multi-scale feature extraction module is designed, which uses convolutional layers of different scales to extract feature information, in order to better capture the characteristics of different fault signals. Secondly, a Self-Calibrated Convolution (SCC) module is constructed. This module automatically adjusts the weights of the convolutional kernels according to the characteristics of the input data, which enhances the network's perception of the input data. Thirdly, a lightweight channel attention residual module is established, which combines channel attention and residual connections, allowing the network to automatically select channels related to fault features, thereby reducing information redundancy. Finally, the Softmax probability distribution function is used as a classifier to achieve bearing fault classification. By using the bearing data set of CWRU for experiment and comparison, it is verified that the method still has strong fault diagnosis performance under variable load and variable noise.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".