Fault Diagnosis of Electric Machines Using Techniques Based on Frequency Domain
Bibliographic record
Abstract
Most of the fault detection techniques used in real time fault detection in power systems is time-domain based. The over current, over voltage, earth fault, impedance relays, and so forth are mostly time-domain based. The fault frequencies to be detected can be simply derived by replacing the fundamental frequency with Low-order harmonics in the equations describing these frequencies. Stator faults usually progress from incipient to a very advanced stage in a matter of seconds. Like stator faults, rotor faults also have been reported to be detectable using odd harmonics in the motor terminal voltage at motor switch-off. Eccentricity faults are related to deformation of air-gaps of an electric machine. Low-order harmonic components that appear in the line voltage of inverters supplying induction motors can provide additional information on the fault of induction machines. Voltage unbalance and machine asymmetries that also change the negative sequence current and impedance can cause misdiagnosis when faults involve only a few turns.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".