Data-Driven Inter Turn Short Circuit Fault Detection of a Segmented SRM Based on Multi-Path Convolutional Neural Network and fCWT
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Bibliographic record
Abstract
This paper aims to propose a fault detection model for inter-turn short circuit faults in a Segmented Switched Reluctance Motor. To this end, a method is developed in the input of which the raw current signal of the motor is processed by the fast Continuous Wavelet Transform (fCWT) to generate the input matrix for the two-dimensional convolution layer. Compared with the conventional wavelet transforms, this method has proved to be considerably faster. The resulting matrix is input into a novel multi-path Convolutional Neural Network (CNN). This model uses a multi-path block which prevents the unintended elimination of crucial features for fault detection by using the feature map construction of the multi-path of layers. To evaluate this method, a six-phase SSRM is simulated using FEM simulation under healthy conditions and different levels of ITSC fault. Then, the current is acquired in a dataset and used for training and testing the model.
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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 | 0.000 |
| 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 it