Compound Dimension Wavelet Network and Its Application in Bearings Fault Diagnosis Under Varying Speeds
Bibliographic record
Abstract
Neural networks have been widely applied in the field of bearing fault diagnosis. However, many existing studies focus on bearings with constant rotational speeds, and there is a lack of research on neural networks for diagnosing faults in bearings with varying speeds. In practice, bearings are always working under varying rotational speeds. This paper proposes a one-dimensional and two-dimensional hybrid neural network combined with wavelet transform for bearings fault diagnosis under variable speeds. Instead of using one-dimensional convolutional kernels, wavelets are employed as generating a two-dimensional feature map that can represent the relationship between signal time and frequency, and the two-dimensional convolutional layer extracts features from the output of the one-dimensional convolutional layer, which is the time-frequency representation obtained through wavelet transformation of the signal. The feasibility of the proposed method is validated on a publicly available dataset from the University of Ottawa.
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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".