An Overview of Noise-Vibration-Harshness Analysis for Induction Machines and Permanent Magnet Synchronous Machines
Bibliographic record
Abstract
Noise-vibration-harshness (NVH) analysis is becoming increasingly important to comprehensively understand, identify, and mitigate the vibration and noise of the electric machines. NVH analysis can also be used for the diagnosis of design defects and mechanical problems. This paper presents a comprehensive overview of modeling and analysis of NVH for induction machines (IMs) and permanent magnet synchronous machines (PMSMs) available in existing literature. The major components involved in NVH analysis are the electromagnetic force calculation, modal analysis, and multiphysics model based NVH synthesis, which will be thoroughly reviewed. Electromagnetic force calculation is vital to the overall analysis since the corresponding forces are the main source resulting in vibration and acoustic noise. Then, approaches for the modal analysis are reviewed to obtain the inherent characteristic of the machine structure and are used for solving the vibration problems. Subsequently, a multiphysics model based NVH analysis is established to evaluate the vibration and noise of electric motors. The synthesis integrates the electromagnetic, structural and acoustic radiation models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".