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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".