An energy kurtosis demodulation technique for signal denoising and bearing fault detection
Why this work is in the frame
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Bibliographic record
Abstract
Rolling element bearings are commonly used in rotary machinery. Reliable bearing fault detection techniques are very useful in industries for predictive maintenance operations. Bearing fault detection still remains a very challenging task especially when defects occur on rotating bearing components because the fault-related features are non-stationary in nature. In this work, an energy kurtosis demodulation (EKD) technique is proposed for bearing fault detection especially for non-stationary signature analysis. The proposed EKD technique firstly denoises the signal by using a maximum kurtosis deconvolution filter to counteract the effect of signal transmission path so as to highlight defect-associated impulses. Next, the denoised signal is modulated over several frequency bands; a novel signature integration strategy is proposed to enhance feature characteristics. The effectiveness of the proposed EKD fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions.
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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 | 0.001 |
| 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