Data-Driven Inter Turn Short Circuit Fault Detection of a Segmented SRM Based on Multi-Path Convolutional Neural Network and fCWT
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".