Smart Sensor-Based Synergistic Analysis for Rotor Bar Fault Detection of Induction Motors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. A smart sensor-based technology is proposed in this article to synergistically use vibration and current harmonics for rotor bar fault detection in IMs. The vibration signal is used for analysis of shaft speed variations and the current harmonics information is applied for rotor bar fault detection. A wireless smart sensor network is developed and used for data collection, allowing for low-cost, low space footprint, and noninvasive installation. The effectiveness of the proposed synergistic technique is examined experimentally, with results demonstrating advantages over conventional methods in terms of accurately differentiating between a healthy and faulty motor, as well as estimating the fault severity, even under zero-load IM conditions. A means to quantify the fault states as diagnostic indices is also proposed for online IM health condition monitoring.
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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 | 0.001 |
| 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 it