Modal identification of machine tool spindle units by output only operational modal analysis
Bibliographic record
Abstract
Accurate tracking of modal characteristics is a valuable diagnostic tool for condition monitoring of machine tool spindle units. While experimental modal analysis (EMA) is the conventional method used for machine tool modal identification, it is often impractical to implement in production settings due to the invasive and manual nature of the impact hammer test. In this study, a new technique for operational modal analysis (OMA) based on output-only vibration measurements obtained during a milling operation with variable spindle speed is proposed. Modal identification is performed using two OMA standard methods, namely stochastic subspace identification (SSI) and frequency domain decomposition (FDD). The modal characteristics are compared to values obtained from conventional EMA from impulse hammer testing on the static spindle, and from the operational spindle during cutting using force measurements collected by a table dynamometer. The percentage difference between the natural frequencies identified by the proposed OMA method and frequencies identified by conventional impulse hammer testing was less than 10%, and for the operational spindle during cutting tests, the difference was less than 3%. These results demonstrate the validity of a new modal identification method that can be practically implemented in production.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".