Structural health monitoring based on sensitivity vector fields and attractor morphing

2006 ◽  
Vol 364 (1846) ◽  
pp. 2515-2538 ◽  
Author(s):  
Shih-Hsun Yin ◽  
Bogdan I Epureanu
Keyword(s):  
Structural Damage ◽  
Vector Fields ◽  
Duffing Oscillator ◽  
Dynamic Responses ◽  
Third Order ◽  
Aeroelastic System ◽  
Aeroelastic Model ◽  
Local Reduction ◽  
Unsteady Supersonic Flow ◽  
Sensitivity Vector

The dynamic responses of a thermo-shielding panel forced by unsteady aerodynamic loads and a classical Duffing oscillator are investigated to detect structural damage. A nonlinear aeroelastic model is obtained for the panel by using third-order piston theory to model the unsteady supersonic flow, which interacts with the panel. To identify damage, we analyse the morphology (deformation and movement) of the attractor of the dynamics of the aeroelastic system and the Duffing oscillator. Damages of various locations, extents and levels are shown to be revealed by the attractor-based analysis. For the panel, the type of damage considered is a local reduction in the bending stiffness. For the Duffing oscillator, variations in the linear and nonlinear stiffnesses and damping are considered as damage. Present studies of such problems are based on linear theories. In contrast, the presented approach using nonlinear dynamics has the potential of enhancing accuracy and sensitivity of detection.

Structural Health Monitoring of a Nonlinear Aeroelastic System Based on Attractor Analysis

2004 ◽  
Author(s):  
Shih-Hsun Yin ◽  
Bogdan I. Epureanu
Keyword(s):  
Health Monitoring ◽  
Structural Damage ◽  
Duffing Oscillator ◽  
Third Order ◽  
Aeroelastic System ◽  
Aerodynamic Loads ◽  
Unsteady Aerodynamic ◽  
Aeroelastic Model ◽  
Local Reduction ◽  
Unsteady Supersonic Flow

The dynamic response of a thermo-shielding panel forced by unsteady aerodynamic loads and a archetypal Duffing oscillator are investigated to detect structural damage. A nonlinear aeroelastic model is obtained for the panel by using third order piston theory to model the unsteady supersonic flow which interacts with the panel. To identify damage, we analyze the shape of the attractor of the dynamics of the aeroelastic system and the Duffing oscillator. Measurements are obtained by simulation at only one location along the panel. Damages of various locations, extents and levels are shown to be revealed by the attractor-based analysis. For the panel, the type of damage considered is a local reduction in the bending stiffness. For the Duffing oscillator, variations in the linear and nonlinear stiffnesses and damping are considered as damage. Most of the current studies of such problems are based on linear theories. In contrast, the results presented are obtained using nonlinear dynamics, and have the advantage of an increased accuracy and sensitivity.

scholarly journals Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Nizar Faisal Alkayem ◽  
Maosen Cao ◽  
Minvydas Ragulskis
Keyword(s):  
Structural Damage ◽  
Model Updating ◽  
Complex Problem ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Fe Model ◽  
Modal Strain Energy ◽  
3D Structures ◽  
Multiobjective Particle Swarm Optimization ◽  
Damage Tracking

Structural damage detection is a well-known engineering inverse problem in which the extracting of damage information from the dynamic responses of the structure is considered a complex problem. Within that area, the damage tracking in 3D structures is evaluated as a more complex and difficult task. Swarm intelligence and evolutionary algorithms (EAs) can be well adapted for solving the problem. For this purpose, a hybrid elitist-guided search combining a multiobjective particle swarm optimization (MOPSO), Lévy flights (LFs), and the technique for the order of preference by similarity to ideal solution (TOPSIS) is evolved in this work. Modal characteristics are employed to develop the objective function by considering two subobjectives, namely, modal strain energy (MSTE) and mode shape (MS) subobjectives. The proposed framework is tested using a well-known benchmark model. The overall strong performance of the suggested method is maintained even under noisy conditions and in the case of incomplete mode shapes.

DAMAGE DETECTION OF RC SLABS USING NONLINEAR VIBRATION FEATURES

2009 ◽  
Vol 09 (04) ◽  
pp. 687-709 ◽  
Author(s):  
XINQUN ZHU ◽  
HONG HAO
Keyword(s):  
Reinforced Concrete ◽  
Nonlinear Vibration ◽  
Structural Damage ◽  
Concrete Structures ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Reinforced Concrete Structures ◽  
Vibration Frequencies ◽  
Skeleton Curve ◽  
Time Frequency

Studied herein are the signatures of nonlinear vibration characteristics of damaged reinforced concrete structures using the wavelet transform (WT). A two-span RC slab built in 2003 was tested to failure in the laboratory. Vibration measurements were carried out at various stages of structural damage. The vibration frequencies, mode shapes, and damping ratios at each loading stage were extracted and analyzed. It is found that the vibration frequencies are not sensitive to small damages, but are good indicators when damage is severe. The dynamic responses are also analyzed in the time–frequency domain by WT and the skeleton curve is constructed to describe the nonlinear characteristics in the reinforced concrete structures. The results show that the skeleton curves are good indicators of damage in the reinforced concrete structures because they are more sensitive to small damages than vibration frequencies.

Finding the jerk properties of multi-body systems using helicoidal vector fields

2008 ◽  
Vol 222 (11) ◽  
pp. 2217-2229 ◽  
Author(s):  
J Gallardo-Alvarado ◽  
H Orozco-Mendoza ◽  
R Rodríguez-Castro
Keyword(s):  
Reference Frame ◽  
Vector Fields ◽  
Time Derivative ◽  
The Body ◽  
Body System ◽  
Third Order ◽  
Numerical Examples ◽  
Multi Body

In this contribution, the kinematic angular and linear third-order properties, also known as jerk analysis, of a multi-body system are determined applying the concept of helicoidal vector fields. The reduced acceleration state, or accelerator, of the body of interest, with respect to a reference frame, is obtained as the time derivative, via a helicoidal field, of the velocity state, also known as the infinitesimal twist. Following that trend, the reduced jerk state, or jerkor, is obtained as the time derivative of the accelerator. The computation of the instantaneous centre of jerk, with its corresponding ellipsoid of jerk, is also included. The expressions thus obtained are extended systematically to multi-body systems. Two numerical examples are provided in order to illustrate the potential of the presented method.

High-Sensitivity Mass Sensing Based on Enhanced Nonlinear Dynamics and Attractor Morphing Modes

2006 ◽  
Author(s):  
Shih-Hsun Yin ◽  
Bogdan I. Epureanu
Keyword(s):  
Lead Zirconate Titanate ◽  
Vector Fields ◽  
Experimental System ◽  
High Sensitivity ◽  
Lead Zirconate ◽  
Chaotic Regime ◽  
Nonlinear Feedback ◽  
Regime Changes ◽  
Distribution Of Points ◽  
Sensitivity Vector

This paper demonstrates two novel methods for identifying small parametric variations in an experimental system based on the analysis of sensitivity vector fields (SVFs) and probability density functions (PDFs). The experimental system includes a smart sensing beam excited by a nonlinear feedback excitation through two PZT (lead zirconate titanate) patches symmetrically bonded on both sides at the root of the beam. The nonlinear feedback excitation requires the measurement of the dynamics (e.g. velocity of one point at the tip of the beam) and a nonlinear feedback loop, and is designed such that the beam vibrates in a chaotic regime. Changes in the state space attractor of the dynamics due to small parametric variations can be captured by SVFs which, in turn, are collected by applying point cloud averaging (PCA) to points distributed in the attractors for nominal and changed parameters. Also, the PDFs characterize statistically the distribution of points in the attractors. The differences between the PDFs of the attractors for different changed parameters and the baseline attractor can provide different attractor morphing modes for identifying variations in distinct parameters. The experimental results based on the proposed approaches show that very small amounts of added mass at different locations along the beam can be accurately identified.

Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure

2016 ◽  
Vol 16 (6) ◽  
pp. 711-731 ◽  
Author(s):  
Yun-Lai Zhou ◽  
Nuno M.M. Maia ◽  
Rui P.C. Sampaio ◽  
Magd Abdel Wahab
Keyword(s):  
Damage Detection ◽  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Analysis ◽  
Structural Damage ◽  
Real Life ◽  
Dynamic Responses ◽  
Hierarchical Clustering Analysis ◽  
Structural Damage Detection ◽  
Structural Dynamic

Maintenance and repairing in actual engineering for long-term used structures, such as pipelines and bridges, make structural damage detection indispensable, as an unanticipated damage may give rise to a disaster, leading to huge economic loss. A new approach for detecting structural damage using transmissibility together with hierarchical clustering and similarity analysis is proposed in this study. Transmissibility is derived from the structural dynamic responses characterizing the structural state. First, for damage detection analysis, hierarchical clustering analysis is adopted to discriminate the damaged scenarios from an unsupervised perspective, taking transmissibility as feature for discriminating damaged patterns from undamaged ones. This is unlike directly predicting the structural damage from the indicators manifestation, as sometimes this can be vague due to the small difference between damaged scenarios and the intact baseline. For comparison reasons, cosine similarity measure and distance measure are also adopted to draw out sensitive indicators, and correspondingly, these indicators will manifest in recognizing damaged patterns from the intact baseline. Finally, for verification purposes, simulated results on a 10-floor structure and experimental tests on a free-free beam are undertaken to check the suitability of the raised approach. The results of both studies are indicative of a good performance in detecting damage that might suggest potential application in actual engineering real life.

Neural Network-Based Method for Structural Damage and Scour Estimation Using Modal Parameters and Dynamic Responses

2020 ◽  
Author(s):  
Shuqing Wang ◽  
Yufeng Jiang
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Structural Damage ◽  
Wind Farms ◽  
Dynamic Responses ◽  
Beam Model ◽  
Noisy Environment ◽  
Modal Parameter ◽  
Onshore Wind ◽  
Acceleration Signals

Abstract Wind energy is the most promising clean, renewable energies to the power industry in the world. More and more wind turbine structures equipped with the larger capacity, taller towers, and longer blades were installed at the offshore/onshore wind farms. But these structures face many harsh environmental conditions, and structural damage and foundation scour are continuously accumulated. It could alter the modal parameter and dynamic response and further reduce the safety of structures. It is a significant challenge on how to accurately estimate the structural states if there is structural damage or foundation scour. For addressing these limitations, a One Dimensional Convolutional Neural Network (1D CNN) method is developed to estimate the structural state. After the Fast Fourier Transform of the acceleration signals, these frequency responses are used as the input to train the 1D CNN, while these states are estimated as the output. A simplified spring-beam model is introduced to simulate the pile-soil interaction, and the effects of the damage and scour on natural frequencies are investigated and compared. The effectiveness and robustness of the proposed 1D CNN method have been numerically investigated by several scenarios associated with the wind turbine structure. Results demonstrate that the 1D CNN method can accurately estimate the structural states, even under a noisy environment. Further, the 1D CNN method can identify the location of damage and scour depth with very high accuracy. This approach may be useful in the on-site structural health monitoring in the wind turbine structure.

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