Static and Dynamic Eccentricity Fault Detection and Quantification in an Inverter-Fed Reluctance Synchronous Machine Using Machine Learning
Bibliographic record
Abstract
Inverter-driven Reluctance Synchronous Machines (RSMs) pose unique diagnostic challenges due to pronounced harmonic distortions and noisy operating conditions, complicating accurate eccentricity fault classification and severity estimation. To address these issues, this paper proposes an ensemble Hierarchical Convolutional Neural Network (HCNN) designed to directly analyze raw stator current waveforms combined with time-delay embeddings, eliminating the need for manual feature extraction. Experimental data from an inverter-driven RSM, subjected to static and dynamic eccentricity faults under various severities and loading conditions, were collected in a laboratory environment to train and validate the model. Load information was initially included during training to provide essential contextual details about the machine's operating state, as preliminary results showed significant improvements in fault classification and severity estimation with explicit load data. However, the proposed ensemble HCNN achieved consistently excellent classification accuracy and severity estimation performance even without explicit load information, suggesting reduced sensitivity to load variations. This highlights the practical value of the ensemble method for reliable fault monitoring in realistic inverter- fed operating environments.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".