Rotor fault detection in induction motors using the fast orthogonal search algorithm
Bibliographic record
Abstract
This paper presents a method of detecting rotor faults in induction motors using the fast orthogonal search (FOS). Proper online condition monitoring of induction machines is very important to ensure safe operation, timely maintenance, and efficiency. It has been shown that when a fault occurs in the rotor, it will exhibit itself as a series of sidebands around the fundamental frequency in the stator current, which can be detected using a spectrum analyzer. Conventional methods based on the fast Fourier transform (FFT) are inadequate for motors operating under light load because the fault signatures will be close to the fundamental. Therefore a balance between resolution and sampling time must be achieved, which is difficult with the FFT. The higher degree of resolution of FOS makes it a promising choice for broken bar detection in motors operating under light load, and the reduction in sampling time is beneficial for motors that are prone to transient conditions. Experimental results using a 1/4 horsepower motor with a rotor winding fault are presented to verify this approach.
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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 | 0.001 |
| 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".