Inclined Eccentricity Fault Diagnosis in Induction Motors Using Deep Learning
Bibliographic record
Abstract
Deep learning recently has proven effective for fault diagnosis in induction motors. Compared to traditional methods, it offers better accuracy and provides an automated process for fault detection. However, a key challenge with using deep learning model is the collection of data, which can be difficult and expensive to obtain on real-world motors. To address this issue a precise simulation model can be used, replicating the induction motors under various fault conditions and thus, data can be generated for various motor configuration and fault conditions. Therefore, in this paper an analytical model based on the modified winding function method is employed to simulate inclined eccentricity faults in a wound rotor induction motor. These faults are characterized by nonuniform air gap and axial misalignment. The data generated from this analytical model is then used to train a five-layer feedforward neural network. To validate the model's performance, predictions are made using data acquired from a scaled-down 2.5 MW wound rotor induction motor, where controlled eccentricity is introduced via a dual screw mechanism. Results demonstrate that the deployed model achieves 97 % accuracy in predicting fault occurrence within this experimental setup.
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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 | 0.000 |
| 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 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".