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Non-parametric nonlinearity detection under broadband excitation

Kolluri, Murali Mohan
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573224392534571

Abstract Details

2019, PhD, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Efforts to develop a general framework to quantify nonlinearities have increased in the past few decades owing to a surge in the applicability of structures that exhibit nonlinearity. There are several excellent methods for system identification that can be used when the functional form of the nonlinearity is known or can be adequately guessed through analytical models. There is, however, a need for a black-box method that can detect, localize and characterize nonlinear behavior from experimental data obtained from multiple input multiple output systems, which can then complement any of the aforementioned identification algorithms. The methods that exist in the current state of the art require case-specific testing and, usually, significant excitation. A method that could circumvent both these issues would find widespread application in structural dynamics testing as an initial indicator for the presence of a nonlinearity. One such method based on the reverse path formulation has been presented in this dissertation. It makes use of the fact that the nonlinearities present can be modeled as internal restoring forces which are at least partially uncorrelated with the input force. The algorithm is shown to successfully find and localize the nonlinearity present on an array of numerical models and experimental setups when subjected to broadband input without assigning any parameters to the same. A means to isolate the uncorrelated spectrum resulting from leakage, which is a signal processing based nonlinearity, from the overall orthogonal projection spectrum has been presented and validated on experimental datasets.
Randall Allemang, Ph.D. (Committee Chair)
Allyn Phillips, Ph.D. (Committee Member)
S. Michael Spottswood, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
Yongfeng Xu, Ph.D. (Committee Member)
169 p.
Mechanical Engineering
Nonlinearity detection; Cyclic averaging; Orthogonal projection; System identification; Leakage

Recommended Citations

Citations

  • Kolluri, M. M. (2019). Non-parametric nonlinearity detection under broadband excitation [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573224392534571

    APA Style (7th edition)

  • Kolluri, Murali Mohan. Non-parametric nonlinearity detection under broadband excitation. 2019. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573224392534571.

    MLA Style (8th edition)

  • Kolluri, Murali Mohan. "Non-parametric nonlinearity detection under broadband excitation." Doctoral dissertation, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573224392534571

    Chicago Manual of Style (17th edition)

ucin1573224392534571
364
© 2019, some rights reserved.Creative Commons License Non-parametric nonlinearity detection under broadband excitation by Murali Mohan Kolluri is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.
Release 3.2.12