Monitoring and diagnosis of faults in interior permanent magnet motors using discrete wavelet transform
Bibliographic record
Abstract
This paper presents a novel application of the discrete wavelet transform (DWT) based algorithm for detecting and diagnosing various disturbances caused by electrical faults in the three-phase interior permanent magnet (IPM) motor. The DWT coefficients of fault currents of different levels of resolution using a selected mother wavelet are transformed to the root mean square (RMS) values through the determination of signal energies of different frequency bands of the DWT. The RMS values are then used in a rule-based classifier (RBC) in order to differentiate between different faulted and normal conditions. The criterion for the fault detection is the comparison of the DWT coefficients of the fifth level details (d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> ) of fault currents using a selected mother wavelet with a threshold determined experimentally during the healthy condition of the motor. The complete protection technique incorporating the proposed DWT based diagnosis algorithm is implemented in real-time using the ds1102 digital signal processor board for a laboratory 1-hp IPM motor. It is found that the proposed DWT based protection algorithm is very fast, and has responded at the instant or within one cycle of the fault occurrence in all cases of investigated faults.
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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 | 0.000 |
| 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 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".