Operational transfer path analysis: Practical Considerations for Selecting Sensor Positions
Bibliographic record
Abstract
Operational transfer path analysis (OTPA) is an alternative to classical transfer path analysis (TPA) as a method used to predict the noise or vibration source and/or path contributions to the response of a system. Both methods are based on the assumption that there is a linear relationship between input (reference) and response positions; however, while the classical TPA mehtod uses a known input force to compute the transfer functions as FRF's, which are then multiplied by a known input force to compute the contributions to the response at the reciever position, the OTPA method uses operational measureable quantities to compute both the transfer functions (in this case transmissibilities) and the response to the receiver. Although OTPA is currently used predominantly for vehicle noise, vibraiton and harshness (NVH) assessment, the method is useful for any noise/vibration assessment where a ranking of the source and/or path contributions at the reciver position(s) is desired, e.g. industrial installations, building HVAC installations, complex machinery and appliances, trains, aircraft, ships, construction equipment. The goal of this paper is to introduce the underlying theory behind the OTPA method, as well as to highlight some practical considerations for selecting sensor positions and the OTPA post-processing. The practical considerations are highlighted through the description of a case study and by recreating the results of the case study via a simple numerical model.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".