An enhanced Hilbert–Huang transform technique for bearing condition monitoring
Why this work is in the frame
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Bibliographic record
Abstract
A new technique, enhanced Hilbert–Huang transform (eHHT), is proposed in this work for fault detection in rolling element bearings. It includes two processes: firstly, the collected vibration signal is denoised to highlight defect-related impulses; and secondly the denoised signal is further processed by the use of the proposed eHHT technique to identify the defect features for bearing fault detection. Signal denoising is carried out by the use of the minimum entropy deconvolution filter to reduce impedance effect of the transmission path of the measured signal. In the proposed eHHT, a novel strategy is proposed to enhance feature extraction based on the analysis of correlation and mutual information. The effectiveness of the proposed eHHT technique in feature extraction and analysis is verified by a series of experimental tests corresponding to different bearing conditions. Its robustness is examined by using data sets from a different resource.
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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