Structural damage identification using feedback control with principal component analysis

2009 ◽  
Author(s):  
Jiann-Shiun Lew
Keyword(s):  
Principal Component Analysis ◽  
Feedback Control ◽  
Structural Damage ◽  
Damage Identification ◽  
Principal Component ◽  
Component Analysis ◽  
Structural Damage Identification

An Optimal Sensitivity-Enhancing Feedback Control Approach via Eigenstructure Assignment for Structural Damage Identification

2007 ◽  
Vol 129 (6) ◽  
pp. 771-783 ◽  
Author(s):  
L. J. Jiang ◽  
J. Tang ◽  
K. W. Wang
Keyword(s):  
Feedback Control ◽  
Frequency Shift ◽  
Natural Frequency ◽  
Structural Damage ◽  
Closed Loop ◽  
Damage Identification ◽  
Frequency Measurement ◽  
Sensitivity Enhancement ◽  
Stiffness Reduction ◽  
Structural Damage Identification

The concept of using sensitivity-enhancing feedback control to improve the performance of frequency-shift-based structural damage identification has been recently explored. In previous studies, however, the feedback controller is designed to alter only the closed-loop eigenvalues, and the effect of closed-loop eigenvectors on the sensitivity enhancement performance has not been considered. In this research, it is shown that the sensitivity of the natural frequency shift to the damage in a multi-degree-of-freedom structure can be significantly influenced by the placement of both the eigenvalues and the eigenvectors. A constrained optimization problem is formulated to find the optimal assignment of both the closed-loop eigenvalues and eigenvectors, and then an optimal sensitivity-enhancing control is designed to achieve the desired closed-loop eigenstructure. Another advantage of this scheme is that the dataset of frequency measurement for damage identification can be enlarged by utilizing a series of closed-loop controls, which can be realized by activating different combinations of actuators in the system. Therefore, by using this proposed idea of multiple sensitivity-enhancing feedback controls, we can simultaneously address the two major limitations of frequency-shift-based damage identification: the low sensitivity of frequency shift to damage effects and the deficiency of frequency measurement data. A series of case studies are performed. It is demonstrated that the sensitivity of natural frequency shift to stiffness reduction can be significantly enhanced by using the designed sensitivity-enhancing feedback control, where the optimal placement of closed-loop eigenvectors plays a very important role. It is further verified that such sensitivity enhancement can directly benefit the damage identification accuracy and robustness.

Research for Structural Damage Identification Method Based on Stable Time Series and Principal Component Analysis

2014 ◽  
Vol 578-579 ◽  
pp. 1020-1023
Author(s):  
Jing Zhou Lu ◽  
Jia Chen Wang ◽  
Xu Zhu
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Damage Detection ◽  
Structural Damage ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Identification Accuracy ◽  
Frame Structure ◽  
Ar Model

In this paper, we introduce a set of techniques for time series analysis based on principal component analysis (PCA). Firstly, the autoregressive (AR) model is established using acceleration response data, and the root mean squared error (RMSE) of AR model is calculated based on PCA. Then a new damage sensitive feature (DSF) based on the AR coefficients is presented. To test the efficacy of the damage detection and localization methodologies, the algorithm has been tested on the analytical and experimental results of a three-story frame structure model of the Los Alamos National Laboratory. The result of the damage detection indicates that the algorithm is able to identify and localize minor to severe damage as defined for the structure. It shows that the suggested method can lead to less amount of computing time, high suitability and identification accuracy.

scholarly journals Study on Structural Characteristics of Composite Smart Grille Based on Principal Component Analysis

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Kong Fanxiao ◽  
Yao Huazhong ◽  
Xie Weidong
Keyword(s):  
Principal Component Analysis ◽  
Structural Damage ◽  
Nearest Neighbor ◽  
Structural Characteristics ◽  
Principal Component ◽  
Composite Plates ◽  
Component Analysis ◽  
Triangular Grid ◽  
Grid Structure ◽  
Good Ability

In recent years, many scholars have conducted in-depth and extensive research on the mechanical properties, preparation methods, and structural optimization of grid structural materials. In this paper, the structural characteristics of composite intelligent grid are studied by combining theoretical analysis with experiments. According to the existing conditions in the laboratory, the equilateral triangular grid structure experimental pieces were prepared. In this paper, principal component analysis combined with nearest neighbor method was used to detect the damage of composite plates. On this basis, the multiobjective robustness optimization of the structure is carried out based on artificial intelligence algorithm, which makes the structure quality and its sensitivity to uncertain parameters lower. Particle swarm optimization (PSO) is used in neural network training. The damage characteristics of different grid structures, different impact positions, and different impact energies were studied. The results show that the structural damage types, areas, and propagation characteristics are very different when the structure is impacted at different positions, which verifies that the grid structure has a good ability to limit the damage diffusion and shows that the grid structure has a good ability to resist damage.

scholarly journals Principal Component Analysis Method with Space and Time Windows for Damage Detection

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2521 ◽  
Author(s):  
Ge Zhang ◽  
Liqun Tang ◽  
Licheng Zhou ◽  
Zejia Liu ◽  
Yiping Liu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Structural Damage ◽  
Time Windows ◽  
Principal Component ◽  
Element Model ◽  
Component Analysis ◽  
Previous Method ◽  
Beam Model ◽  
Space And Time

Long-term structural health monitoring (SHM) has become an important tool to ensure the safety of infrastructures. However, determining methods to extract valuable information from large amounts of data from SHM systems for effective identification of damage still remains a major challenge. This paper provides a novel effective method for structural damage detection by introduction of space and time windows in the traditional principal component analysis (PCA) technique. Numerical results with a planar beam model demonstrate that, due to the presence of space and time windows, the proposed double-window PCA method (DWPCA) has a higher sensitivity for damage identification than the previous method moving PCA (MPCA), which combines only time windows with PCA. Further studies indicate that the developed approach, as compared to the MPCA method, has a higher resolution in localizing damage by space windows and also in quantitative evaluation of damage severity. Finally, a finite-element model of a practical bridge is used to prove that the proposed DWPCA method has greater sensitivity for damage detection than traditional methods and potential for applications in practical engineering.

Sign in / Sign up

Export Citation Format

Share Document