Wavelet spectrum analysis for bearing fault diagnostics
Why this work is in the frame
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Bibliographic record
Abstract
A new signal processing technique, wavelet spectrum analysis, is proposed in this paper for incipient bearing fault diagnostics. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. A novel strategy is suggested for the deployment of the wavelet centre frequencies. A weighted Shannon function is proposed to synthesize the wavelet coefficient functions to enhance feature characteristics, whereas the applied weights are from a statistical index that quantifies the effect of different wavelet centre frequencies on feature extraction. An averaged autocorrelation spectrum is adopted to highlight the feature characteristics related to bearing health conditions. The performance of this proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this new signal processing technique is an effective bearing fault detection method, which is especially useful for non-stationary feature extraction and analysis.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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