Phaselet-Based Method for Detecting Electric Faults in $3\phi$ Induction Motor Drives—Part I: Analysis and Development
Bibliographic record
Abstract
This paper presents the analysis and development of a new method for electric fault detection in induction motor drives. The proposed method is based on extracting the magnitudes and phases of the high-frequency subband (HFSB) contents present in the d - q-axis components of the stator currents (idand iq) in an induction motor. The desired magnitudes and phases are extracted by processing id and iq using phaselet frames that are realized by a modulated filter bank. This filter bank is designed using six digital high-pass filters, whose coefficients are determined by biorthogonal phaselet basis functions. Extracted HFSB contents provide signature information that can offer accurate and fast detection of faults. The phaselet-based electric fault detection method has been made into a procedure for digital implementation. The performance of the proposed method is evaluated offline for collected stator currents from two different induction motor drives under different operating conditions. Offline test results show accurate, reliable, and fast detection of electric faults, with minor sensitivity to the ratings, loading levels, and type and location of faults.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".