Penentuan Jumlah Minimal Line Of Resolution Dalam Spektrum Vibrasi Untuk Pengukuran Rutin Vibrasi
Bibliographic record
Abstract
Resolution of vibration spectrum is one of that affect the accuracy of vibration analysis result. This study was conducted to determine the most effective minimum line of resolution (LOR) for determining the target resolution in separating two close peaks. Several parameter settings like Fmax and LOR are used to measure the spectrum of a model rotor. The results of this study indicate that with 3 LORs in the separating frequency, the spectrum is quite detailed in separating two close peaks. This experimental study resulted a simple calculation in determining the minimum amount of LOR for routine vibration measurements. ABSTRAK Salah satu hal yang berpengaruh terhadap ketepatan analisa data vibrasi adalah resolusi grafik spektrum vibrasi. Penelitian ini dilakukan untuk mengetahui jumlah minimal line of resolution (LOR) yang paling efektif untuk menentukan target resolusi dalam memisahkan dua buah peak yang berdekatan. Beberapa setting parameter seperti Fmax dan LOR digunakan untuk melakukan pengukuran spektrum dari suatu rotor model. Hasil penelitian ini menunjukkan bahwa dengan 3 buah LOR dalam separating frequency, spektrum yang dihasilkan yang cukup detail dalam memisahkan dua buah peak yang berdekatan. Studi eksperimen ini menghasilkan perhitungan sederhana dalam penenentuan jumlah minimal LOR untuk pengukuran rutin vibrasi.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".