Amplitude-based multiscale reverse dispersion entropy: a novel approach to bearing fault diagnosis
Bibliographic record
Abstract
The multiscale fluctuation dispersion entropy algorithm (MFDE) is widely used to extract the characteristics from a variety of complex nonlinear signals, including bearing signals, due to its excellent performance to quantify the uncertainties of complex nonlinear systems. However, limited by the classification number and coarse-graining process, the periodic impulses generated by the defect point cannot be effectively detected by MFDE, restraining the characterization abilities of entropy features and resulting in undesirable diagnosis results for bearing faults. To overcome the disadvantages of MFDE, an amplitude-based multiscale dispersion entropy (AMDE) is proposed in this paper. The AMDE utilizes the phase scale factor to calculate multiple groups of amplitude difference series that contain different amplitude information. As such, the amplitude compression caused by the large-scale factor in traditional coarse-graining process is avoided, and the calculated entropy features not only characterize the irregularity of the whole signal but also reflect the changes of the impulse components. Afterwards, the perception range and the sensibility of AMDE are expanded and enhanced for amplitude variation, and the coarse-graining process and Gaussian reference are used to obtain multi-dimensional reversed entropy features. Combining those steps, the amplitude-based multiscale reverse dispersion entropy (AMRDE) algorithm is proposed. Finally, the capability of the proposed algorithm to track the amplitude variation and fluctuation is successfully demonstrated by analyzing noisy signals and amplitude-modulated signal. Meanwhile, the features extracted from bearing signals demonstrated that it is effective to use AMRDE to represent the health conditions of rolling bearing. Therefore, the entropy metric calculated by AMRDE can be the useful indicator in the fields of mechanical equipment fault diagnosis, structural health monitoring, and so on.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".