Online parameter estimation and loss calculation using duplex neural — Lumped parameter thermal network for faulty induction motor
Bibliographic record
Abstract
A stator winding fault in one phase of induction motor (IM) gives rise to higher harmonics distortion, increased torque ripple, temperature rise in the magnetic material, mechanical vibrations due to varying magnetic forces and magnetic noise. The fault leads to a change in the electromagnetic field generated in the motor as compared to the normal operation of motor. The copper losses generated in stator increases, thus leading to overall increase in the temperature of the motor. Looking from the aspect of electrical equivalent circuit model, the parameters of the motor changes due the occurrence of fault, which makes it difficult for designing a drive for the motor. In this paper a novel computational model has been presented which uses both artificial neural network model (ANN) and lumped parameter thermal network (LPTN) for parameter estimation and calculation of losses which can be used for designing a fault-tolerant, loss minimizing drive. This dual network model has been and experimented on a 7.5 hp aluminum-rotor induction motor.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".