Ensemble of extreme learning machines for diagnosing bearing defects in non-stationary environments under class imbalance condition
Why this work is in the frame
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Bibliographic record
Abstract
Two practical inevitabilities for diagnostic systems are the abilities of incremental learning in non-stationary environments and diagnosing under the class imbalance condition. The class imbalance condition has been widely occurred in real applications where system usually works in the normal state and it is not easy to collect the representative patterns of faulty classes. This work aims to adapt two state-of-the-art ensemble-based techniques for incremental learning and diagnosing faults in non-stationary environments under the class imbalance. These techniques train several extreme learning machines to create the ensemble which can incrementally learn the relation between features and faults in various class-imbalanced chunks of data collected from non-stationary environments. These diagnostic schemes are applied to diagnose bearing defects in induction motors.
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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