An Auto Instantaneous Frequency Order Extraction Method for Bearing Fault Diagnosis under Time-Varying Speed Operation
Why this work is in the frame
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Bibliographic record
Abstract
Bearing fault diagnosis under variable speed usually have confronted two obstacles: a) blurry time frequency representation (TFR) and thus unavailable instantaneous frequency (IF) for resampling, and b) errorprone resampling process. To address such problems, this paper proposes a method which consists of two main steps: a) a regional peak search algorithm which searches the frequency bins point by point at local frequency regions is developed to extract the IF from the TFR of the original signal, and b) with the accurate IF estimator (either shaft IF, instantaneous fault characteristic frequency (IFCF) or their harmonics), an order peak highlighting strategy is exploited via multi-demodulating the signal and superposing the resulted frequency spectra of all demodulated signal components which are acquired by adaptive band-pass filtering. Then the instantaneous frequency order (IFO) of signal components of interest contained in the original signal can be highlighted and the IFO spectrum can be obtained for bearing fault diagnosis. In this manner, the bearing fault can be diagnosed without the tachometer, predenosing and resampling involved, indicating that the proposed can substantially reduce human involvement and facilitate its implementation in a fault detection expert system. The effectiveness of the proposed method are validated by both simulated and experimental data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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