Oscillatory behavior based fault feature extraction for bearing fault diagnosis
Bibliographic record
Abstract
An intelligent fault signature extraction scheme based on oscillatory behaviors is reported in this paper for bearing fault diagnosis. The proposed method is based on the joint application of morphological component analysis (MCA) and tunable Q-factor wavelet transform (TQWT) to decompose a signal into two signal components (i.e., low- and high-oscillation components) according to whether they having sustained oscillations. As bearing fault-induced transients (low-oscillation component) oscillate differently from periodic interferences and noise (high-oscillation component and residual), they can be separated via the MCA with the aid of TQWT which is parameterized by Q-factor and plays a role of distinguishing signal components presenting different oscillatory behaviors. The low- and high-oscillation components can be obtained by solving the objective function formulated based on MCA and TQWT. The determination of Q-factor for each signal component representation is also explored in this paper. The effectiveness of the proposed method is examined by experimental data.
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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 | 0.001 |
| 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) | 0.001 | 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".