A normalized Hilbert-Huang transform technique for bearing fault detection
Bibliographic record
Abstract
Bearings are commonly used in rotary machinery, whereas up to half of machinery malfunctions could be related to bearing defects. Unfortunately reliable fault detection systems still remain a challenging task, especially when bearing defect-related features are nonstationary. A new normalized Hilbert-Huang transform (NHHT) technique is proposed in this paper for vibration-based bearing fault detection. The NHHT for bearing fault detection takes two processes: firstly the vibration signal is denoised to highlight defect-related impulses; and secondly representative features are extracted for bearing fault detection. Vibration signal denoising is carried out by the use of the maximum kurtosis deconvolution filter to reduce impedance effect of transmission path of the measured vibration signal. A novel strategy based on D’Agostino-Person normality is suggested to enhance the distinctive intrinsic mode functions for representative features extraction and formulation for bearing fault detection. The effectiveness of the proposed NHHT technique is verified by a series of experimental tests corresponding to different bearing health conditions, and its robustness in bearing fault detection is examined by the use of data sets from a different experimental setup.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".