Adaptive incremental ensemble of extreme learning machines for fault diagnosis in induction motors
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an adaptive incremental ensemble of extreme learning machines for fault diagnosis. The diagnostic system contains a data processing unit which aims to progressively generate discriminant features from the vibration signals for decision making. The decision making unit receives a few sets of labeled discriminant features in a chunk by chunk manner, incrementally learns the features-faults relations, dynamically diagnoses multiple bearing defects, and adaptively adjusts itself to learn new concept classes. This adaptive ensemble system is based on incremental learning of multiple extreme learning machines that are able to consult together and adjust themselves based on their confidence in the decision making. Extreme learning machines are used to construct the hybrid ensemble due to their good controllability and fast learning rate. Experimental results show the efficiency of the hybrid diagnostic system. The proposed diagnostic system is applied to diagnosing bearing defects in an induction motor.
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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