End-to-End Deep Learning for Classification and Quantification of Mixed Eccentricity Faults in Salient Pole Synchronous Machines
Bibliographic record
Abstract
This study introduces a Hierarchical Convolutional Neural Network (HCNN) for the classification and severity estimation of Mixed Eccentricity (ME) faults in Salient Pole Synchronous Machines (SPSMs). The HCNN extracts patterns from raw voltage and current waveforms, automatically learning fault features without manual preprocessing. Time Delay (TD) embeddings were specifically incorporated into the HCNN to reconstruct underlying system dynamics and effectively capture temporal dependencies and nonlinearities inherent in the input signals. Conventional methods typically rely on extensive signal preprocessing to identify these subtle fault patterns but often struggle due to the sensitivity of preprocessing techniques to noise and overlapping harmonic components. The HCNN achieved perfect classification accuracy for ME faults, and regression accuracies of $90.16 \%$ and $99.93 \%$ were obtained for severity estimation using the voltage and current-based input scenarios, respectively. The results indicate that HCNNs can effectively detect and quantify ME faults in SPSMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".