Amplitude-Based Time-Shift Multiscale Feature Fuzzy Dispersion Entropy: A Novel Health Indicator for Aeroengine Fault Diagnosis
Bibliographic record
Abstract
Fuzzy dispersion entropy (FuzzyDE) effectively solves the dispersion pattern rounding errors of dispersion entropy (DE). However, distinct pattern characteristics correspond to different properties of dynamic systems. The pattern probability derived from merely counting the number of different patterns imposes limitations on the representation of nonlinear systems by FuzzyDE. To address this issue, this article presents the feature FuzzyDE (FFuzzyDE). This approach utilizes the numerical characteristics of dispersion patterns as new indicators for measuring pattern probability, thereby replacing the singular quantification methods of traditional approaches. The fuzzy membership is combined with the characteristics of the patterns to derive the exclusive probability associated with each dispersion pattern. Simulation experiments demonstrate that the feature dispersion pattern construction scheme allows FFuzzyDE to achieve stable entropy values more rapidly across varying data lengths, is sensitive to characteristic changes in frequency-amplitude modulation signals, effectively captures subtle characteristic changes in nonlinear systems, and exhibits excellent noise suppression capabilities. Furthermore, by integrating the amplitude-based time-shift coarse-graining method, FFuzzyDE is extended into the multiscale calculation domain, resulting in amplitude-based time-shift multiscale feature fuzzy DE (ATSMFFDE). Two sets of bearing fault damage experiments show that ATSMFFDE can accurately extract bearing fault characteristics, and therefore provides effective health monitoring indicators for aeroengine bearings.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".