Enhancing Reliability in Induction Machines: A Focus on Stator Inter-Turn Fault Diagnosis
Bibliographic record
Abstract
Induction Machines (IM) are widely favored in contemporary industry due to their affordability, robustness, and economical maintenance requirements. Despite these advantages, IMs are susceptible to various faults, such as bearing, stator winding, and rotor issues, which can cause significant operational disruptions, production delays, and considerable economic losses. Addressing these concerns, the importance of early fault diagnosis cannot be overstated, as it significantly improves machine reliability and efficiency, prevents further damage, and decreases the dependency on extensive repairs post-breakdown. The study focuses on early detection of Stator Inter-turn Faults (SITF) in IMs through an online diagnostic approach, utilizing a dataset from both Healthy and Faulty IMs equipped with a 2.2kW four-pole squirrel cage. An exploratory analysis has been performed on the dataset to simplify the data structure and understand feature dynamics. The most straightforward classification method has been applied to identify SITF occurrences. The effectiveness of each diagnostic technique was evaluated by comparing their accuracy levels. The research successfully developed both non-neural and neural network models capable of detecting SITFs with minimal severity, demonstrating the potential for enhancing industrial operations through early fault diagnosis.
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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 | 0.001 |
| 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".