Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a new bearing defect diagnostic and classification method is proposed based on pattern recognition of statistical parameters. Such a pattern recognition problem can be described as transformation from the pattern space to the feature space and then to the classification space. Based on trend analysis of six commonly used statistical parameters, four parameters, namely, RMS, Kurtosis, Crest Factor, and Impulse Factor, are selected to form a pattern space. A 2-D feature space is formulated by a nonlinear transformation. An intraclass transformation is used to cluster the data of different bearing defects into different regions in the feature space. The classification space is constructed by piecewise linear discriminant functions. Training the classification space is performed, in this paper, by using data of bearings with seeded defects. Diagnosis of the defected bearings in the classification space then becomes straightforward. Numerical experiments show that the proposed method is effective in indicating both the location and the severity of bearing defects.
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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