Physics-Informed Neural Network Approach to Diagnosing Uniform Demagnetization Faults in Permanent Magnet Synchronous Machines
Bibliographic record
Abstract
Permanent Magnet (PM) Demagnetization (PMD) faults in Permanent Magnet Synchronous Machines (PMSMs) pose significant challenges to their reliability and performance, necessitating advanced diagnostic techniques for early detection to prevent catastrophic failures and minimize downtime. This paper proposes a Physics-Informed Neural Network (PINN) approach for diagnosing PMD faults in PMSMs. The PINN integrates the dynamical model of the PMSM with measured data to estimate the PM flux linkage under healthy and uniform PMD conditions, thereby quantifying the severity of the PMD. By incorporating the physical equations of the motor, the PINN offers higher data efficiency and enhanced robustness compared to existing conventional data-driven methods. The estimation of PM flux linkage is validated through both simulation data obtained from a finite element analysis (FEA) model of the PMSM and experimental measurements from a laboratory test rig under steady-state and dynamic operating conditions. These evaluations demonstrate the method’s capability to accurately quantify the severity of demagnetization faults. Furthermore, the robustness of the proposed approach to measurement noise and model parameter variations and its data efficiency are assessed. Finally, a hyperparameter tuning strategy tailored for PINNs operating in parameter estimation mode is introduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".