Fault Diagnosis in Variable-Frequency-Drive-Fed Induction Motors: Addressing Stator Turn-to-Turn and High-Resistance-Connection Faults
Bibliographic record
Abstract
Detection and differentiation of stator turn-to-turn (T-T) and high-resistance-connection (HRC) faults in induction motors (IMs) are challenging due to varying severities and fault signatures because of noise introduced by variable-frequency-drives (VFDs). This paper introduces an online technique to diagnose and differentiate T-T and HRC faults in medium/high voltage IMs. It is also capable of identifying the faulty phase during these two faults. The proposed approach identifies abnormal conditions by calculating Fault Diagnose Index using the Teager-Kaiser Energy of the current unbalance factor derived from the supply side current quantities. Discrimination between T-T and HRC faults is confirmed by observing the phase angle between the negative sequence current from the supply-side and remote-side. Faulty phase detection is carried out using the angle of the Faulty Phase Detector determined by the phase angle of the mean difference between the voltage and current unbalance factors. The proposed approach is validated through multiple test cases derived from a laboratory prototype of a VFD-connected IM. Results show that the proposed technique identifies and discriminates between T-T and HRC faults with variable fault resistance and load conditions, outperforms existing techniques and assists in faulty phase identification for effective maintenance and reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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 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".