High-fidelity fault signature extraction of rolling bearings via nonconvex regularized sparse representation enhanced by flexible analytical wavelet transform
Bibliographic record
Abstract
Diagnosing the bearing fault, especially incipient fault is important for equipment health management while is still a challenge in which high-fidelity extraction of the fault signature is expected. A method termed flexible analytical wavelet transform (FAWT)-enhanced sparse representation with nonconvex regularization is proposed in this research. FAWT enjoys flexible covering along both the frequency and time axis as well as tunable oscillation property and is adopted to well match the fault impulses after parameters optimization. In the fabricated FAWT-enhanced sparse model with generalized minimax-concave regularization, an index termed harmonic-to-noise energy ratio of envelope spectrum (ES-HNER) is proposed which is found effective and robust for quantitative assessment of the richness of fault signature and could be automatically evaluated from the envelope spectrum, based on which the parameters for constructing the FAWT basis and threshold are optimized via maximizing the ES-HNER in the candidate parameters space. The sparse decomposition signals are further obtained via solving the FAWT-enhanced sparse model, upon which the bearing fault signature is expected to be exhibited on the envelope spectrum. The performance of the proposed method has been validated via analysis of both simulation and experiment signals as well as comparison with other methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".