Anomaly Detection for Induction Motors Based on Unsupervised Online Machine Learning
Bibliographic record
Abstract
Induction motors are essential to industrial operations, making effective condition monitoring crucial for preventing costly failures and minimizing unplanned downtime. Among the most critical issues, inter-turn short circuits in stator windings pose severe risks, as they can quickly escalate and cause extensive damage to motor components if left unaddressed. This study presents a novel unsupervised learning methodology for detecting such anomalies in Squirrel Cage Induction Motors (SCIMs). The proposed approach utilizes clustering technique based on the Gaussian Mixture Model (GMM) applied to three-phase stator current signals, with the Bayesian Information Criterion (BIC) dynamically determining the optimal number of clusters. This capability enables the method to effectively differentiate between normal operation and fault conditions without requiring additional sensors, complex signal preprocessing, or high-frequency sampling. A Finite Element Model (FEM) of the motor is developed to simulate various fault scenarios with varying severity levels, enabling comprehensive evaluation of the proposed methodology. The results demonstrate the robustness and accuracy of the technique, emphasizing its practicality as a reliable and efficient solution for real-time condition monitoring in industrial applications.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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 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".