On line trained fuzzy logic and adaptive continuous wavelet transform based high precision fault detection of IM with broken rotor bars
Bibliographic record
Abstract
This paper presents an online trained fuzzy logic and adaptive wavelet based high precision fault detection of broken rotor bars for squirrel cage induction motor (IM). Motor faults which consist of broken rotor bars, bearing decay, eccentricity, etc. appears as different frequencies in the stator current signals. The winding function is used to obtain stator current and speed signals at different fault and load conditions. These signals are analysed through the adaptive continuous wavelet transform (CWT) to detect the amplitudes and frequency components corresponding to different fault and load conditions. The coefficients of CWT are adapted online based on the harmonics amplitude, which are the output of CWT. These amplitudes and frequencies are applied to train a fuzzy logic controller (FLC) in simulation. Then the adaptive CWT and trained FLC are applied to detect the fault condition of a large size motor in both simulation and realtime. The experimental results found that the proposed adaptive CWT and FLC based fault detection method can detect the motor fault conditions accurately. Thus, the proposed method could be a potential candidate to detect the motor fault, especially for large size industrial motors.
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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".