A DFFT and Coherence Analysis-Based Fault Diagnosis Approach for Induction Motors Fed by Variable Frequency Drives
Bibliographic record
Abstract
For faults diagnosis in a Variable Frequency Drive (VFD)-fed induction motor, a Discrete Fast Fourier Transform (DFFT) and coherence analysis-based approach is proposed in this paper. To identify signature harmonics that maintain a strong correlation between a healthy and a faulty cases and are present under various conditions, a coherence analysis is conducted. After signature harmonics are identified, fault diagnosis can be carried out by comparing magnitudes of the fundamental and signature harmonics under various healthy and faulty conditions. Magnitudes of the fundamental voltage and the third harmonic voltage can serve as parameters to detect the five types of faults. The fifth harmonic current can effectively detect the occurrence of a fault although it cannot distinguish the fault types. The combination of the fundamental voltage and the third harmonic voltage from the stator voltage and the fifth harmonic current from the stator current can lead to effective fault diagnosis. The proposed approach is verified using two motor loading conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".