A progressive decomposing and double screening strategy of VMD for weak fault extraction of hoisting machinery
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Bibliographic record
Abstract
To alleviate the difficulty of extracting weak fault features of hoisting machinery, a progressive decomposing and double screening strategy of variational mode decomposition (VMD) is presented in this paper. Firstly, the feasibility and effectiveness of extracting fault modes using progressive decomposition strategy is validated through numerical simulation, and it solves the problem of determining the mode number [Formula: see text] in traditional VMD. Secondly, a new index named energy fluctuation factor (EFF) is proposed. Specifically, EFF is more effective in detecting the signal periodicity compared with the kurtosis and the Shannon entropy (SE), and it is used to optimize the balance parameter [Formula: see text] of VMD. Thirdly, the criterion of double screening based on the kurtosis and the EFF is given to accurately localize and reconstruct the fault modes, and then Hilbert transform is utilized to demodulate the reconstructed mode. Finally, the numerical simulation and experimental and practical engineering applications verify that the proposed method can accurately extract the modes of weak fault and well solve the problem of determining the key parameters (i.e., [Formula: see text] and [Formula: see text] ) of VMD. Furthermore, the superiority of the proposed method is validated by comparing with other fault diagnosis methods.
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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 | 0.000 |
| 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 it