FAULT CLASSIFICATION FOR A CLASS OF TIME-VARYING SYSTEMS BY USING OVERLAPPED ART2A NETWORKS
Why this work is in the frame
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Bibliographic record
Abstract
Fault diagnosis currently offers different alternatives to classify faults at early stages, such as model-based and knowledge-based techniques. Nevertheless, fault classification for time-varying systems is still an open problem. Strategies such as self-organizing maps and principal component analysis ensure fault classification to bounded time-variance faults. The approach presented in this paper proposes the use of three non-supervised neural networks. The first two networks overlapped by certain time shift. The third network performs a comparison between the two networks outputs in the previous stage. As a result, the system classifies the fault even if the system is time-variant. The strategy named as Overlapped ART2A Network, aims to obtain an autonomous performance and on-line fault classification. Results show the effectiveness of the approach considering a case study with fault and fault-free scenarios.
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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) | 0.001 | 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