Broken Rotor Bar Fault Diagnosis for Induction Motors Using Power Spectral Density and Complex Continuous Wavelet Transform Methods
Bibliographic record
Abstract
Induction motors are widely used in various industrial sectors, fault diagnosis of induction motors are critical to prevent equipment failure and production downtime. In this paper, a stator current signature analysis method is proposed for squirrel cage induction motors’ broken rotor bar (BRB) fault diagnosis. Two different techniques are implemented: Power Spectral Density (PSD) based stator currents’ amplitude spectrum analysis; and one dimensional Complex Continuous Wavelet Transform (CWT) based stator currents’ time-scale spectrum analysis using Complex Morlet Wavelet (CMW). The performance of the two techniques are compared using experimental stator current data measured in a lab for a 0.25 HP induction motor. The stator current under healthy and faulty states of the motor were measured, the faults include one, two and three BRBs. For 2 and 3 BRB faults, the holes were drilled on the rotor bars 90 degree apart. Two loading conditions of the motor were used during the measurement, 30% and 85%. It is found that the CWT has better performance than the PSD estimates for the BRB fault detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".