Induction motor fault detection using vibration and stator current methods
Bibliographic record
Abstract
Induction motors are widely used in industry as prime electromechanical energy conversion devices. Consequently, the condition monitoring and fault diagnosis of induction motors have received significant attention recently and become an integrated part of various maintenance strategies (for example preventive, condition-based and reliability-based maintenance). This paper presents a comparison of results of induction motor broken rotor bar fault detection using vibration and stator current methods. A broken rotor bar fault was induced into in a variable speed three-phase induction motor. Both the vibration and stator current signatures were acquired under different speed and load conditions. The fault detection sensitivities of vibration and stator current methods are evaluated. This paper also addresses the relationship between current and vibration signatures under normal and faulty motor conditions using correlation and frequency response methods. This relationship is desirable in order to determine the fault signature transmission mechanism and to exclude the irrelevant vibration sources so as to enhance fault detection accuracy. The relationship, studied during steady-state operation and start-up, enabled the identification of the vibrations from other sources.
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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 | 0.000 |
| 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".