Study and detection of demagnetization in line start permanent magnet synchronous machines using artificial neural network
Why this work is in the frame
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Bibliographic record
Abstract
This paper makes an effort to study the causes and effects of permanent magnet demagnetization in permanent magnet machines and hence proposes an exclusive artificial neural network (ANN) based permanent magnet demagnetization detection scheme. A laboratory 2.8 kW line-start permanent magnet synchronous machine (LSPMSM) is used in the numerical investigations for initiating permanent magnet demagnetization and detecting the fault. Firstly, experiments were performed on the machine to determine it parameters and understand its steady-state and dynamic performance using a developed position sensor and an experimental setup. A mathematical model of the machine was then developed using the d-q axis theory to analyze the behavior of the machine under healthy and demagnetization conditions. Later, an ANN based detection scheme is proposed and verified through numerical investigations. The results obtained from the investigations are thus analyzed.
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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