A rolling bearing fault diagnosis method based on a new data fusion mechanism and improved CNN
Bibliographic record
Abstract
The development of modern industry has accelerated the need for intelligent fault diagnosis. Nowadays, most bearing fault diagnosis methods only use the information of one sensor, and the diagnostic knowledge contained in single-sensor data is often insufficient, which leads to insufficient diagnostic accuracy under complex working conditions. In addition, although convolutional neural network (CNN) has been widely used in fault diagnosis, the network structures used are still relatively traditional, and the ability of feature extraction is relatively poor. To solve the problems, firstly, this paper innovatively uses coordinate attention (CA) to more fully mine fusion information after concatenate (Cat) operation and proposes a new data fusion mechanism, Cat-CA. Then an improved Residual Block is proposed, and a novel improved CNN is built by stacking this Block. Finally, the Cat-CA-ICNN is built by combining Cat-CA and improved CNN, and its effectiveness and superiority are verified using two datasets.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 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".