Fault Diagnosis of Electric Motors by a Novel Convolutional-based Neural Network and STFT
Bibliographic record
Abstract
This paper proposes a novel method for fault detection in electric motors based on Short-Time Fourier Transform (STFT) and a regulated convolutional-based neural network. In this method, STFT is applied over the raw signal measured from the motor to generate a 2D matrix as an input to a classification model. The classification model constitutes a regulated network combining Convolutional Long Short Term Memory (ConvLSTM) andConvolutional Neural Network (CNN) which is developed to be fit for the low-size 2D STFT matrices. The proposed method is evaluated over, a Permanent Magnet Synchronous Motor (PMSM) with healthy and three levels of Inter-Turn Short Circuit (ITSC) fault conditions. The model is also tested under the influence of measurement noise. The model has shown a good performance for all these conditions.
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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".