Advanced Diagnosis of Air Gap Eccentricity in Three-Phase Induction Motor Using DWT Decomposition and AI Techniques
Bibliographic record
Abstract
Early fault detection for the induction machine became a necessity to prevent the escalation of failures to severe levels, thereby avoiding unscheduled downtimes.Among the various failure modes in electrical machines, rotor-related faults, such as air gap eccentricity, require particular attention and to detect this type of defects model-based methods are extensively used in this field.However, because of the intricacies of the diagnosed model and the time-consuming investigations it renders the diagnosis process more laborious and less efficient.This article focuses on applying a non-model based approach that relies in general on feature extraction using discrete wavelet transform decomposition analysis of stator current signal for various stages of air gap eccentricity and under multiple operating conditions and as a first step of the conducted work, through performing an in-depth energy distribution analysis through all of the decomposed signal levels to extract the best sub-signal level that holds the most relevant information about the machine's condition alongside to RMS values of the signal.The second part of the research focuses on employing the extracted features as input data used for training a multi-layer perceptron algorithm such as support vector machine and decision trees.Our endeavor is to choose the most accurate algorithm for the multiclass classification.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".