Machine Learning-Based Severity Assessment and Incipient Turn-to-Turn Fault Detection in Induction Motors
Bibliographic record
Abstract
A two-layer soft voting ensemble machine learning based algorithm for the diagnosis of turn-to-turn faults in the stator winding of three-phase induction motors is presented. The suggested approach offers severity assessment and faulty phase identification considering recurrence qualification analysis features, extracted from recurrence plot images generated using the Max-Min difference technique from raw signals. Thereafter, the proposed model is implemented with eight machine learning classifiers that undergo training with extracted features utilizing a 10-fold cross-validation technique. Subsequently, predictions of each layer are aggregated through soft voting. Datasets required for training and validation are gathered from a laboratory-based experimental hardware setup of induction motor, covering various turn-to-turn fault severity considering multiple loading and fault resistance. Performance of the proposed algorithm is verified by considering various performance metrics. Comparative results demonstrate that the proposed classifier outperforms individual machine learning classifiers for turn-to-turn fault diagnosis along with severity and faulty phase detection, which in turn assists in reducing downtime and maintenance costs.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".