Variable-Speed Bearing Fault Diagnosis Based on BDVMD, FRTSMFrBSIE, and Parameter-Optimized GRU-MHSA
Why this work is in the frame
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Bibliographic record
Abstract
To address the challenges of feature extraction and low classification accuracy in fault diagnosis of variable-speed rolling bearings, this paper proposes an intelligent fault diagnosis method based on bandwidth division variational mode decomposition (BDVMD), fractional domain time-shift multiscale fractional Boltzmann-Shannon interaction entropy (FRTSMFrBSIE), and parameter-optimized gated recurrent unit with multi-head self-attention (GRU-MHSA). First, the BDVMD is introduced to decompose and reconstruct signals, obtaining high-quality reconstructed fault signals. Next, the FRTSMFrBSIE is proposed to calculate the entropy of the reconstructed signals and generate a fault feature dataset. Subsequently, the improved dung beetle optimization (IDBO) algorithm is applied to optimize the parameters of the GRU-MHSA model, adaptively determining its optimal configuration. Finally, the fault feature dataset is input into the optimized model for fault classification, achieving a classification accuracy of 98.75%. Experiments conducted on the Ottawa bearing dataset validate the proposed method, and the results demonstrate its effectiveness and superiority in feature extraction and fault classification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it