Detection of Induction Motor Faults: A Comparison of Stator Current, Vibration and Acoustic Methods
Bibliographic record
Abstract
In this paper we present the comparison results of induction motor fault detection using stator current, vibration, and acoustic methods. A broken rotor bar fault and a combination of bearing faults (inner race, outer race, and rolling element faults) were induced into variable speed three-phase induction motors. Both healthy and faulty signatures were acquired under different speed and load conditions. To address the detection capabilities of the above methods, comparisons are made in both the time and joint time-frequency domains. In the frequency domain, spectral differences are compared and characterized under constant speed conditions. To evaluate the detection sensitivities under non-stationary conditions (e.g. startup), a joint time-frequency method called the smoothed pseudo Wigner-Ville distribution (SPWVD) is employed to analyze non-stationary signatures. The SPWVD is a powerful technique for revealing non-stationary characteristics of motor signatures. Experimental results show that the stator current method is sensitive to the broken rotor bar fault while the vibration method is sensitive to bearing faults. The acoustic method is very attractive in that it contains less noise and interference within the analyzing frequency band. With the proper selection of monitoring and analysis methods, induction motor faults can be detected accurately under both stationary and non-stationary states.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".