Novel Hybrid Framework for Detection, Discrimination, and Classification of Stator Winding Faults in Induction Motors
Bibliographic record
Abstract
This paper presents a hybrid fault diagnosis framework that integrates the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d–q–0</i> transformation with a Temporal Convolutional Attention Network (TCAN) for detecting, discriminating, and classifying stator winding faults in induction motors (IMs). Initially, the Modified-Discrete Fourier Transform algorithm is applied for phasor estimation of acquired current signals, while the phase angle is estimated from voltage signals using a Synchronous Reference Frame Phase-Locked Loop. Thereafter, the estimated phase angle and current phasors from both the supply and remote ends are transformed into their corresponding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q-0</i> components. After extracting domain-specific features of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q-0</i> components, the TCAN model has been trained using 10-fold cross-validation. The experimental data were gathered from a laboratory setup capable of simulating various fault severities under different load and fault resistance conditions, including external faults with current transformer saturation. The effectiveness of the presented technique is assessed using standard evaluation metrics and compared with other deep learning models such as CNN, LSTM, GRU and TCN. Results display superior accuracy and fault discrimination capability of the presented approach, highlighting its robustness and reliability for accurate classification of healthy, internal, and external fault situations.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".