A Multi-dilated Fusion Convolutional Neural Network for Fault Diagnosis of Rolling Bearings
Bibliographic record
Abstract
It is of great significance to implement real-time and effective condition monitoring and fault diagnosis for rolling bearings. Traditional model-based approaches rely heavily on previous experience and expert knowledge, which hinders their practicality in the industrial field. To cope with this problem, we develop a novel CNN mode called a multi-dilated fusion convolutional neural network (MF-CNN) for bearing fault diagnosis in this study. First of all, a CNN model with 1-D convolutional operations is developed to learn features directly from vibration signals. Then, a multi-dilated fusion module (MDFM) is developed to guide the CNN model to extract features from vibration signals at multiple levels. MDFM adopts dilated convolutions with different dilation rates to enlarge the receptive field. Finally, the MF-CNN architecture is built based on the above improvements. Some experiments are carried out to verify the effectiveness of the proposed MF-CNN. Experimental results suggest that MF-CNN outperforms some state-of-the-art approaches.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".