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 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 | 0.001 |
| 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 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".