Multichannel multiscale increment entropy and its application in roller bearing fault diagnosis
Bibliographic record
Abstract
As a new index to measure the complexity of time series, increment entropy, which takes into account the fluctuation directions and amplitude of time series, has better performance than the traditional entropy analysis methods such as sample entropy and permutation entropy. However, the increment entropy value of time series at single scale cannot completely reflect the dynamic change of time series. In this paper, the multichannel multiscale increment entropy (MMIE) is proposed by introducing the coarse-graining and multichannel analysis tools of time series to explore the complexity of multichannel time series over multiple different scales. MMIE considers the dynamic relationships between multichannel data and the relevant cross-channel variations that are often overlooked in single-channel analysis, thus achieving a full utilization of state information. Finally, an MMIE-based fault diagnosis method is proposed for roller bearing. The analysis results of the simulation signals and the measured data of roller bearings indicate that MMIE performs better than MMDE, MMPE, and MMSE approaches in time costing and fault identification rates.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".