Bi-filter multiscale-diversity-entropy-based weak feature extraction for a rotor-bearing system
Bibliographic record
Abstract
Abstract Multiscale-based entropy methods have proven to be a promising tool for extracting fault information due to their high feature extraction ability and easy application. Despite multiscale analysis showing great potential in extracting fault characteristics, it has some drawbacks, such as cutting the data length and neglecting high-frequency information. This paper proposes a bi-filter multiscale diversity entropy (BMDE) to filter comprehensive fault information and address the data length problem. First, the low-frequency information is filtered out by moving average in a multi-low procedure and the high-frequency information is filtered out by an adjacent subtraction in a multi-high procedure. Second, a modified coarse-grained process is introduced to overcome the issue of data length. The validity of the BMDE method is evaluated using both simulation signals and experimental measurements. Results demonstrate that the proposed method offers optimal feature extraction capability with the highest diagnostic accuracy compared with four other traditional entropy-based diagnosis methods.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".