A Variable Speed Fault Detection Approach for Electric Motors in EV Applications based on STFT and RegNet
Bibliographic record
Abstract
Electric motors play a significant role in the power train system in Electric Vehicles (EV s). Therefore, their online condition monitoring is essential in ensuring the reliable operation of the entire powertrain. In EV applications, the motors work in a non-ideal environment and continuously varying operating conditions. Therefore, the fault diagnosis of the EV motors is challenging, and the fault diagnosis model must work in wide ranges of speeds and loads. In this paper, a short-time Fourier transform with varying window is proposed as the current signal processing of the motor along with a convolutional neural-based network for the detection of the interturn short circuit of permanent magnet synchronous motor. The proposed method is evaluated using a simulation dataset and a benchmark bearing fault dataset. by Matlab/Simulink® and the model is trained with the data in four speeds, and the model tests are carried out for a different operating speed.
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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".