A Threshold-Based Stator Inter-Turn Fault Diagnosis for Induction Motors Using the Negative Sequence Current’s Magnitude and Phase Angle
Bibliographic record
Abstract
This paper introduces a novel threshold-based stator inter-turn fault (SITF) detection and faulty phase identification method for induction motors through the negative sequence stator current analysis, where the negative sequence current magnitude level (NCL) is used for SITF detection, and the negative sequence current’s phase angle (∡12) is used for the faulty phase identification. Through the systematic experimental investigation of NCL’s behavior under various unbalanced voltage conditions (0.1% to 1.7%), a detection threshold (ThSITF) of NCL equal to 10% is established. Three angular zones for ∡12 are defined for the faulty phase identification: ±60° for Phase A, 60°−180° for Phase B, and 180°−300° for Phase C. Experimental validations on a 2.2 kW induction motor demonstrates accurate SITFs detection across different fault severities and motor loading levels using the proposed method. Extensive testing results for six SITFs with 2, 3, 5, 10, 13, and 15 shorted turns at Phase B reveal a linear correlation between the fault severity and NCL. The proposed method remains effective for variable frequency drive (VFD)-fed induction motors, showing enhanced sensitivities at a higher frequency.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".