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 thed–q–0transformation 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 correspondingd-q-0components. After extracting domain-specific features ofd-q-0components, 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".