Dynamic Eccentricity Fault Detection in Synchronous Machines Using Principal Component Analysis
Bibliographic record
Abstract
The paper proposes a new method for detecting dynamic eccentricity faults in a Salient Pole Synchronous Machine (SPSM). Several methods have been documented in literature for detecting dynamic eccentricity, which includes using search coils, motor current signature analysis (MCSA), and data-based techniques. The former approach is invasive, thereby increasing installation cost, while MCSA is affected by load variations. A major hurdle in using data-driven methods is the selection of features. To overcome these limitations, the authors propose performing principal component analysis (PCA) on the fundamental sideband frequencies of motor current signals to detect and isolate dynamic eccentricity from static eccentricity faults. Principal Component Analysis (PCA) is a data-driven technique that can extract meaningful features in a dataset by transforming correlated variables into a reduced set of uncorrelated variables through a linear transformation. Experimental data of stator currents from a 2-kW, 208 V, 3-phase SPSM was used for the analysis. Results showed that the method isolated dynamic eccentric faults irrespective of the loading condition of the SPSM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".