Cubic spline interpolation-based refined composite multiscale dispersion entropy and its application to bearing fault identification
Why this work is in the frame
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Bibliographic record
Abstract
As a powerful tool, dispersion entropy (DE) has good capability to measure the irregularity and complexity of nonlinear systems, so it is extensively utilized in the field of structural health monitoring. However, traditional multiscale dispersion entropy (MDE) will suffer from down-sampling as the scale factor increases, which compresses some intrinsic feature information, so that the representation ability of MDE for nonlinear system is significantly restrained. Aiming at this problem, a new method called cubic spline interpolation-based refined composite multiscale dispersion entropy (CSIRCMDE) is proposed. In this frame, the coarse-graining series are regarded as knots to calculate the interpolative points in different sub-sections. Then, the obtained coarse-graining samples and interpolative samples are used to construct interpolation series to capture the potential vibration characteristics and constrain the down-sampling phenomenon. Finally, the first coarse-graining point is gradually shifted back to make multiple groups of interpolation series, and the entropy values are rectified by calculating mean pattern probabilities at the same scale. Through these steps, CSIRCMDE can grasp sufficient feature information to reduce entropy errors and overcome the dependence on sample size compared with traditional algorithms, which is demonstrated by simulation and noise signals. Furthermore, two types of bearing damage experiments prove that CSIRCMDE has good capability to characterize the bearing states, therefore, based on the proposed algorithm, the classification model that can fully identify different bearing faults is established.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it