A Semi-Supervised Diagnostic Framework Based on the Surface Estimation of Faulty Distributions
Why this work is in the frame
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Bibliographic record
Abstract
Design of the data-driven diagnostic systems usually requires to have labeled data during the training session. This paper aims to design a hybrid data-driven framework for diagnosing faults, where the data labels are not available to a large extent. This hybrid framework has five steps for transforming raw vibration signals to informative sets of samples for decision making. It uses several state-of-the-art approaches for feature extraction and semi-supervised feature reduction. The decision-making step uses a number of state-of-the-art semi-supervised learners. This step also comprises a novel surface estimation approach that is developed for SSL. The proposed hybrid framework is applied for diagnosing bearing defects in induction motors and validated based on four scenarios, each of which is experimented with different amounts of labeled samples. The attained diagnostic accuracies show the efficiency of the proposed hybrid framework, including the novel semi-supervised learner in classifying bearing defects, regardless of the number of labeled samples.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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