Early Detection of Stator Inter-Turn and Single Phasing Faults in Induction Motors Using Negative Sequence Voltage Components
Bibliographic record
Abstract
This paper presents a non-invasive threshold-based method for early detection of stator inter-turn faults (SITFs) and single phasing (SP) faults in induction motors by measuring the three-phase voltages at the motor terminal. These voltage signals are processed to extract the sequence components. The Negative Voltage Factor (NVF) is defined as the ratio of the magnitudes of negative sequence voltage |V2| to positive sequence voltage |V1|, and is used as a fault indicator. The proposed method uses a dual-threshold strategy: a lower threshold for SITFs detection and a higher threshold for SP faults detection by comparing with the VNF values. Unlike traditional current-based approaches, this voltage-based technique proves to be more sensitive and load-independent. Simulation results using ANSYS Maxwell and experiments in the lab for a 2.2 kW induction motor demonstrate the method’s effectiveness to detect incipient SITFs and SP faults accurately under various motor loadings and fault severities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".