An Auto Instantaneous Frequency Order Extraction Method for Bearing Fault Diagnosis under Time-Varying Speed Operation
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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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 | 0.001 |
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".