Stator Inter-Turn Fault Detection for Line-Connected Induction Motors Using Convolutional Neural Network
Bibliographic record
Abstract
Stator faults cause one third of induction motor failures. Stator inter-tern faults (SITFs) are especially difficult to detect at the incipient stage. In this paper, a novel fundamental frequency phasor magnitude (FPM)-based SITF detection method using a three-dimensional (3D) convolutional neural network (CNN) is proposed for line-connected induction motors. The novelty of the proposed FPM-CNN method is the mechanism of converting three-phase currents into 3D color images. Online FPMs of three-phase motor currents can be extracted by applying a digital Fourier filter and combined in a given time frame to produce images for the CNN. Based on the selected feature image dimension, the CNN architecture is sketched from scratch to identify SITFs and their severity. A 2.2 kW induction motor was tested under healthy and five SITF cases with 12 loading conditions to acquire experimental datasets. The proposed method shows high accuracy, fast online response, and robustness under unbalanced voltages.
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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 | 0.000 | 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".