10 April 2007 Some data-driven modeling approaches for detecting changes in nonlinear dampers
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Abstract
Various identification methods are compared for full-scale nonlinear viscous dampers, including a parametric approach using a simplified design model (SDM), the non-parametric Restoring Force Method (RFM), and the non-parametric Artificial Neural Network (ANN) approach. Advantages and disadvantages of each method are discussed for monitoring purposes. In the comparison, it is shown that the RFM is superior to other methods in regard to the following aspects: (1) no assumption is needed on the nature of the monitored systems; (2) the method is applicable to a wide range of nonlinear system types; (3) the same identification model can be used for the unknown system changes, including the change of system type as well as the change of system parameter values; and (4) physical interpretation of system changes are possible, using the identified values of the series expansion coefficients. A set of experiments was also conducted using magneto-rheological (MR) dampers to validate the feasibility of system change detection. For small changes in the magnetic field strength, the corresponding changes in the dynamic characteristics of the MR damper were detected, using the identified RFM coefficients.
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Hae-Bum Yun, Hae-Bum Yun, Sami F. Masri, Sami F. Masri, Farzad Tasbihgoo, Farzad Tasbihgoo, Raymond W. Wolfe, Raymond W. Wolfe, } "Some data-driven modeling approaches for detecting changes in nonlinear dampers", Proc. SPIE 6529, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007, 65291R (10 April 2007); doi: 10.1117/12.715860; https://doi.org/10.1117/12.715860
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