Cepstrum-driven modulated empirical wavelet transform and its application in bearing fault diagnosis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Empirical wavelet transform (EWT) has a complete theoretical support and can adaptively separate modes with different characteristics from the frequency domain. Signal decomposition and mode extraction based on the empirical wavelet transform can obtain more accurate components. This paper proposes a modulated empirical wavelet transform driven by cepstrum under the basic framework of traditional EWT method. The most innovative point of this paper is to use the characteristics of cepstrum to update the waveform of trend spectrum and realize the function of separating different modes. The filtering process constructs filter banks covering the entire frequency band based on scaling functions and empirical wavelets. In order to enhance the fault characteristics from the filtering components, the amplitude of its spectrum was modulated based on the Fourier transform characteristics. Finally, the effectiveness of the algorithm is verified by using simulation signals and experimental signals provided by Case Western Reserve University.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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