Multiscale Dynamic Weight-Based Mixed Convolutional Neural Network for Fault Diagnosis of Rotating Machinery
Bibliographic record
Abstract
Convolutional neural network (CNN) was widely applied to the data-driven-based fault diagnosis. However, it often needs to artificially transform the signal into a 2-D image with the help of time-frequency transformation; furthermore, the alternative 1-D CNN can only extract single-scale features but cannot adaptively reveal the relationship between scales even if 1-D convolution kernels of multiple scales are applied at the same time. Accordingly, this article proposes a mixed CNN model, namely, a multiscale dynamic weighted 1-D to 2-D (1D–2D) CNN model (1D–2D MDWCNN), which resembles multiwavelet-based CNN method but uses different scale convolution kernels in 1-D convolution neural network to facilitate the multiscale feature extraction and accomplish adaptive features fusion in the constructed 1D–2D seamless joint network framework. Specially, to reflect the global contribution of different convolution kernels, a dynamic weighted (DW) network layer is constructed to adaptively adjust the global weight of each convolution kernel, so as to improve the fault diagnosis ability. The validity of the model is verified by motor bearing fault diagnosis experiment and gearbox fault diagnosis experiment. The diagnosis results proved the developed 1D–2D MDWCNN model superior to the latest CNN-based models.
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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".