Stochastic Vector Identification and Uncertain Modal Parameter Estimation for a Smart Composite Beam

John D. Hios; Spilios D. Fassois

doi:10.4028/www.scientific.net/AST.56.453

Paper Titles

Smart Nonlinear Acoustic Based Structural Health Monitoring System
p.426

Strain Monitoring of FRP Elements Using an Embedded Fibre Optic Sensor
p.435

High Strain Monitoring during Fatigue Loading of Thermoplastic Composites Using Imbedded Draw Tower Fibre Bragg Grating Sensors
p.441

Self-Detection of Delamination in Active Fiber Composites
p.447

Stochastic Vector Identification and Uncertain Modal Parameter Estimation for a Smart Composite Beam
p.453

Optimization of Signal Pre-Processing for the Integration of Cost-Effective Local Intelligence in Wireless Self-Powered Structural Health Monitoring
p.459

Numerical Simulation for Concrete Structure Health Monitoring Based on Smart Piezoelectric Transducer Array
p.469

Advanced Ultrasonic Structural Monitoring of Waveguides
p.477

Damage Detection in Beam-Like Structures Using Deflections Obtained by Modal Flexibility Matrices
p.483

HomeAdvances in Science and TechnologyAdvances in Science and Technology Vol. 56Stochastic Vector Identification and Uncertain...

Stochastic Vector Identification and Uncertain Modal Parameter Estimation for a Smart Composite Beam

Abstract:

This study aims at identifying the modal characteristics and their uncertainties for a smart composite beam. The problem is addressed via Vector AutoRegressive with eXogenous excitation (VARX) models. The advantages of VARX modeling include simplicity of implementation, high accuracy, parsimony of representation, and capability of handling modal uncertainties. Two different approaches to assess the modal parameter uncertainties are investigated. The first is based upon linearizing the function that relates the VARX model parameters with the modal parameters, whereas the second is based upon computer simulations using the Monte Carlo and the bootstrap schemes. The results indicate that VARX modeling captures the system dynamics and provides accurate modal parameters with tight confidence intervals.