Effect of Friction-Induced Nonlinearity on OMA-Identified Dynamic Characteristics of Offshore Platform Models

The identification of the modal characteristics of engineering systems under operational conditions is commonly conducted with the use of the Operational Modal Analysis (OMA), being a class of useful tools employed within various fields of structural, mechanical as well as marine and naval engineering. The current OMA methods have been advanced on the basis of two fundamental, though, restrictive assumptions: (i) linearity and (ii) stationarity. Nevertheless, there are several applications that are inherently related to various nonlinear mechanisms, which, in turn, violate the two cornerstones of OMA and hence, question its robustness and efficiency. Along these lines, the current study addresses the effect of friction-induced nonlinearity on OMA-identified dynamic characteristics of an experimental set up consisting of a pair of reduced scale offshore platform models that are connected through a friction-based mechanism. Both time-domain and frequency-domain methods were employed to assess the effect of the varying friction-induced nonlinearity on the OMA-identified modal characteristics. The findings of this study reveal that OMA-based methods provide reasonable identification results implying that nonlinear and nonstationary systems can be described by underlying linear systems, even though, in principles, the basic assumptions of linearity and stationarity are violated.

[1]  Carlos E. Ventura,et al.  Introduction to Operational Modal Analysis , 2015 .

[2]  Carlo Rainieri,et al.  Operational Modal Analysis of Civil Engineering Structures: An Introduction and Guide for Applications , 2014 .

[3]  Carlos E. Ventura,et al.  Introduction to Operational Modal Analysis: Brincker/Introduction to Operational Modal Analysis , 2015 .

[4]  Palle Andersen,et al.  Modal Identification from Ambient Responses using Frequency Domain Decomposition , 2000 .

[5]  J. P. Norton,et al.  An Introduction to Identification , 1986 .

[6]  Richard Russell,et al.  A Multi-Input Modal Estimation Algorithm for Mini-Computers , 1982 .

[7]  E. Parloo,et al.  Frequency-domain generalized total least-squares identification for modal analysis , 2004 .

[8]  Tobias Friis,et al.  Operational Modal Analysis Based Stress Estimation in Friction Systems , 2018 .

[9]  Carlos E. Ventura,et al.  Damping estimation by frequency domain decomposition , 2001 .

[11]  Julie Kristoffersen,et al.  Operational modal analysis based prediction of actual stress in an offshore structural model , 2017 .

[12]  P. Guillaume,et al.  Modal Identification Using OMA Techniques: Nonlinearity Effect , 2015 .

[13]  A. Harvey.  Time series models , 1983 .

[14]  Rune Brincker,et al.  Modal identification of output-only systems using frequency domain decomposition , 2001 .

[15]  Shien-Ming Wu,et al.  Time series and system analysis with applications , 1983 .

[16]  Hongli Gao,et al.  Screw performance degradation assessment based on quantum genetic algorithm and dynamic fuzzy neural network , 2015 .

[17]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .