Experimental Investigation of the Combined Fault: Mechanical and Electrical Unbalances in Induction Motors Based on Stator Currents Monitoring
Bibliographic record
Abstract
Numerous studies examine the electrical and mechanical, internal and exterior problems of induction motors separately. However, this type of machines is also susceptible to several combined faults which affect it at the same time. The aim of this work is to study firstly the effects of the mass imbalance with the supply voltage unbalance faults in a combined state, and specifically its effects on the stator currents (distortion of currents waveform and augmentation of current unbalance factor (CUF)). The findings of this research were derived from experimental tests, which enabled us to produce the combined voltage and mass imbalance fault intentionally. The second purpose of this paper is to identify these irregularities. In the field of induction motor fault diagnosis, the stator current analysis techniques have shown to be quite successful and have gained widespread use. Therefore, we used the well-known method motor current signature analysis (MCSA). In addition, we have strengthened the research by using spectral analysis on the Park's components (Id and Iq). The obtained results demonstrate the efficacy of analyzing stator currents and Park components spectra (particularly the direct Park component (Id)) for detecting this kind of electrical and mechanical combined defects
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".