A Hybrid Ensemble Scheme for Diagnosing New Class Defects under Non-stationary and Class Imbalance Conditions
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
Strategic necessities to design and implement practical diagnostic systems are the abilities of incremental learning and diagnosing new class defects under non-stationary and class imbalance conditions. In this work, a hybrid ensemble scheme, named Learn++NCS, is adopted for diagnosing bearing defects in induction motors. This diagnostic scheme includes a feature extraction module and a hybrid ensemble scheme. The former intends to extract discriminant features from the vibrational signals. The latter collects various class imbalance sets of samples chunk by chunk from a non-stationary environment, constructs a hybrid ensemble by means of a consultation and voting mechanism, incrementally learns novel features-defects relations and diagnoses new class defects. Experimental results present the effectiveness of the proposed hybrid scheme.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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