Simplified Machine Diagnosis Techniques under Impact Vibration Using Higher Order Cumulants with the Comparison of Three Cases
Bibliographic record
Abstract
Among many amplitude parameters, Kurtosis (4-th normalized moment of probability density function) is recognized to be the sensitive good parameter for machine diagnosis. On the other hand, a new method of machine diagnosis can be considered utilizing the higher order cumulants which have the characteristics that cumulants more than 3rd order are 0 under Gaussian distribution. Cumulants are stated in combination with the same order moment and the moments under that order. Simple calculation method is required on the maintenance site. Furthermore, the absolute deterioration factor such as Bicoherence would be much easier to handle because it takes the value of 1.0 under the normal condition and tends to be 0 when damages increase. In this paper, nth normalized cumulant is considered so as to intensify the sensitivity of diagnosis. Also, the simplified calculation method for this new parameter by impact vibration is introduced. Furthermore, the absolute deterioration factor is introduced. Three cases in which the rolling elements number is nine, twelve and sixteen are examined and compared. The new calculation method is examined whether it is a sensitive good parameter or not. Compared with the results obtained so far, the new method shows fairly good results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".