Experimental Acoustic Noise and Sound Quality Characterization of a Switched Reluctance Motor Drive with Hysteresis and PWM Current Control
Bibliographic record
Abstract
This paper presents an experimental characterization of acoustic noise and sound quality in a 12/8 Switched Reluctance Motor (SRM) using hysteresis and Pulse Width Modulation (PWM) current control techniques. To overcome the limitations of traditional sound power measurements and enhance the accuracy of acoustic noise evaluation, a setup is applied for calculating sound power based on sound intensity measurements. The study provides a detailed description of the intensity probe-holding fixture, the hardware configuration for acoustic noise experiments, and the software setup tailored to specific measurement requirements. The acoustic noise characteristics of the motor are assessed at various operating points using two distinct current control methods: hysteresis current control with a variable switching frequency of up to 20 kHz and PWM current control with a fixed switching frequency of 12.5 kHz. Measurements of sound pressure and sound intensity enable the calculation of sound power and sound quality metrics under different operating conditions. Furthermore, the study investigates the influence of various factors on the motor’s sound power levels and sound quality. The findings provide valuable insights into the contributions of these factors to acoustic noise characteristics and offer a foundation for improving the motor’s acoustic behavior during the design and control stages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".