Current signature analysis of induction machine rotor faults 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 monitoring of motors is important to ensure safe operation, timely maintenance, and efficiency. One method to detect rotor faults is to monitor the stator current spectrum for fault harmonics. Conventional methods that use the FFT for spectral analysis are inadequate for motors that are prone to transient conditions. Changing operating conditions during the sampling time may cause the spectrum to become smudged, reducing the amplitude of the fault signatures causing them to become undetectable. Reducing the sampling time minimizes the effects of smudging, however a fine resolution must still be maintained to distinguish between harmonics. This is especially important when operating under light-load since the fault signatures will be very close to the fundamental frequency. Therefore a balance between resolution and sampling time must be achieved, which is difficult with the FFT. This paper will demonstrate through simulation that FOS is able to achieve eight times the resolution of the FFT for the same data length. Therefore FOS is a promising choice for rotor fault detection in motors where long periods of steady-state are unavailable, and for motors operating with less than full-load. (5 pages)
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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 | 0.001 |
| 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".