Application of Complex Demodulation for Pseudo-Key-Phasor Recovery From Fast-Response Pressure Measurements
Bibliographic record
Abstract
When digitizing the output of fast-response pressure transducers installed on rotating machinery, it is often desirable to use a phase-synchronized method. The mechanical design of many turbomachines, particularly those not originally designed for a research application, can make it difficult to install the physical key-phasor needed to acquire phase-synchronized measurements. A method of phase-synchronization using a pseudo-key-phasor is presented in this paper. The technique applies complex demodulation to recover a pseudo-key-phasor signal from the blade-passing signal recorded in sampled data. The recovered pseudo-key-phasor is then used to digitally resample the data at a constant phase angle, removing the effect of small rotor speed variations. Example applications of this technique to vibration measurements can be found in the literature; however, examples of application to rotor pressure measurement were not. The technique has been applied to fast-response pressure measurements taken on the shroud of a high speed centrifugal compressor. It was found that this technique was able to remove the effect of rotor speed variations from data sampled with equal time intervals, making them suitable for phase averaging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".