An Application of a Hybrid Wavelet-SVM Based Approach for Induction Motor Fault Detection
Bibliographic record
Abstract
Induction motors have been widely used in the industries due to their simple and rugged construction. Failures of this electrical machinery may cause considerable losses. Therefore adapting an efficient method to diagnose a fault at a very early stage would prevent any further consequences of this deficiency. The major concern is related to the mechanical failures, normally caused by the inner component deficiencies. Application of intelligent methods have attracted interest in recent years. Support Vector Machine is a supervised learning method, based on statistical learning theory. This thesis presents three different SVM algorithms: SVM, KPCA-SVM and ROC-SVM, applicable for broken rotor bars detection. SVM proved to be reliable method for classification. While application of KPCA-SVM, shows nonlinear feature extraction can improve the performance of classifier with respect to reduce the number of overlapping samples. Furthermore, ROC-SVM has improved the accuracy by selecting a decision threshold for the classifier.
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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".