A multibranch residual network for fault-diagnosis of bearings
Bibliographic record
Abstract
Time-frequency domain analysis methods are used to diagnose faults in bearings by extracting the features of fault signals. Given that a fault signal is also a form of audio signal, we extracted the characteristics of the mel spectrum from the original signal and applied it to a convolution neural network proposed in this paper. Focusing on the residual structure in the residual neural network (ResNet), we solved the gradient disappearance problem and accelerated the training of the model. The importance of each feature channel could be estimated adaptively using the squeeze-and-excitation network (SENet) considering the relationships between the channels. We examined the feature map of each layer using a multibranch residual network (MB-ResNet) to characterize the bearing fault signal. We used the multibranch residual structure to reduce the sense field of each residual and added a parallel local sensing module to train the model to recognize the weight of each input feature to either increase or reduce the influence of local features. Our experimental results show that the MB-ResNet is very good at extracting features, is robust, and capable of generalization.
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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 | 0.001 |
| 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".