2017
DOI: 10.1016/j.ymssp.2017.01.020
|View full text |Cite
|
Sign up to set email alerts
|

Model reduction and frequency residuals for a robust estimation of nonlinearities in subspace identification

Abstract: The introduction of the frequency-domain nonlinear subspace identification (FNSI) method in 2013 constitutes one in a series of recent attempts toward developing a realistic, firstgeneration framework applicable to complex structures. If this method showed promising capabilities when applied to academic structures, it is still confronted with a number of limitations which needs to be addressed. In particular, the removal of nonphysical poles in the identified nonlinear models is a distinct challenge. In the pr… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 6 publications
(2 citation statements)
references
References 23 publications
0
2
0
Order By: Relevance
“…In particular, the stabilization of modal masses is included as described in [35,36] along with natural frequencies, damping ratios and mode shapes. More recent developments of NSI use modal decomposition to have a better estimation of the coefficients of the nonlinearities and to remove possible spurious poles from the identified model [35][36][37].…”
Section: Problem Statementmentioning
confidence: 99%
“…In particular, the stabilization of modal masses is included as described in [35,36] along with natural frequencies, damping ratios and mode shapes. More recent developments of NSI use modal decomposition to have a better estimation of the coefficients of the nonlinearities and to remove possible spurious poles from the identified model [35][36][37].…”
Section: Problem Statementmentioning
confidence: 99%
“…This should be considered as a first attempt to identify distributed nonlinearities, which is still an open point in the research community. Two methods are used, both based on subspace identification [5]: TNSI (Time Nonlinear Subspace Identification) [1,6] and FNSI (Frequency Nonlinear Subspace Identification) [7][8]. The main difference between TNSI and FNSI is the domain in which they work, which is time for the first and frequency for the second.…”
Section: Introductionmentioning
confidence: 99%