Structural Damage Detection Using Linear Matrix Inequality Methods

2000 ◽  
Vol 122 (4) ◽  
pp. 448-455 ◽  
Author(s):  
M. O. Abdalla ◽  
K. M. Grigoriadis ◽  
D. C. Zimmerman
Keyword(s):  
Experimental Data ◽  
Linear Matrix Inequality ◽  
Damage Detection ◽  
Structural Damage ◽  
Optimization Problems ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Structural Damage Detection ◽  
Test Bed ◽  
Stiffness Reduction

In this work, linear matrix inequality (LMI) methods are proposed for computationally efficient solution of damage detection problems in structures. The structural damage detection problem that is considered consists of estimating the existence, location, and extent of stiffness reduction in structures using experimental modal data. This problem is formulated as a convex optimization problem involving LMI constraints on the unknown structural stiffness parameters. LMI optimization problems have low computational complexity and can be solved efficiently using recently developed interior-point methods. Both a matrix update and a parameter update formulation of the damage detection is provided in terms of LMIs. The presence of noise in the experimental data is taken explicitly into account in these formulations. The proposed techniques are applied to detect damage in simulation examples and in a cantilevered beam test-bed using experimental data obtained from modal tests. [S0739-3717(00)00104-5]

Structural Damage Detection Based on Autoregressive Spectra Analysis of Time Series

2012 ◽  
Author(s):  
Ming-Hui Hu ◽  
Er-Ding Cong ◽  
Shan-Tung Tu ◽  
Fu-Zhen Xuan ◽  
Shao-Ping Zhou ◽  
...  
Keyword(s):  
Damage Detection ◽  
Autocorrelation Function ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Spectra Analysis ◽  
Power Spectral ◽  
Strain Data ◽  
On Line ◽  
Spectral Analysis Method ◽  
Analysis Of Time Series

In this paper, an autoregressive spectral analysis method is proposed for on-line structural damage detection. Firstly, the monitoring strain data are modeled as ARMA models. The power spectral density (PSD) value is directly determining by center-autocorrelation function and statistical autocorrelation function. Structural damage detection is conducted by comparing the values of PSD of the inspected structure. Two experimental applications to the detection of damage in industrial piping structure demonstrate the potential and effectiveness of the new damage detection technique.

scholarly journals A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Exponential Stability ◽  
External Disturbance ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Input Output ◽  
Lmi Approach ◽  
Dynamic Neural Networks ◽  
Numerical Examples

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

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