Remote vibrometry recognition of nonlinear eigen-states for object coverage of randomly large size
Michael C. Kobold, Michael McKinley

TL;DR
This paper demonstrates that remote vibrometry can effectively recognize nonlinear eigen-states of large objects by using full-structure sensing, revealing more vibration data and mode transition probabilities, which enhances vehicle identification and understanding of system dynamics.
Contribution
It introduces a simulation method for remote vibrometry that captures comprehensive vibration data over entire structures, improving mode detection and transition analysis compared to point measurements.
Findings
Full-structure sensing reveals more vibration modes.
Transition probabilities between modes can be calculated.
Energy of vibration modes decreases with increasing frequency.
Abstract
For objects of "large" vibration size such as waves on the sea surface, the choice of measurement method can create different understandings of system behavior. In one case, laser vibrometry measurements of a vibrating bar in a controlled laboratory setting, variation in probe spot size can omit or uncover crucial structural vibration mode coupling data. In another case, a finite element simulation of laser vibrometry measures a nonlinearly clattering armor plate system of a ground vehicle. The simulation shows that sensing the system dynamics simultaneously over the entire structure reveals more vibration data than point measurements using a small diameter laser beam spot, regardless of the variation of footprint (coverage) boundaries. Furthermore, a simulation method described herein allows calculation of transition probabilities between modes (change-of-state). Wideband results of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
