Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines
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Bibliographic record
Abstract
Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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