Frequency-based damage detection in cantilever beam using vision-based monitoring system with motion magnification technique

2018 ◽  
Vol 29 (20) ◽  
pp. 3923-3936 ◽  
Author(s):  
Andrew Jaeyong Choi ◽  
Jae-Hung Han
Keyword(s):  
Genetic Algorithm ◽  
Damage Detection ◽  
Monitoring System ◽  
Cantilever Beam ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Solution Space ◽  
Mode Shapes ◽  
High Resolution Data ◽  
Motion Magnification

This article proposes a method for damage detection using vision-based monitoring with motion magnification technique. The methods based on the vibration characteristics of structures such as natural frequency, mode shapes, and modal damping have been applied to structural damage detection. However, the conventional methods have limitations for practical applications. Vision-based monitoring system can be employed as a new structural monitoring system because of its simplicity, potentially low cost, and unique capability of collecting high-resolution data. A methodology called video motion magnification has been developed to amplify non-visible small motions in a video to reveal the dynamic response. The video motion magnification method can be applied to measure small displacements to calculate the natural frequencies and the operational deflection shapes of the structures. Unlike conventional optimization methods, a genetic algorithm explores the entire solution space and can obtain the global optimum. In this article, identification of the location and magnitude of damage in a cantilever beam is formulated as an optimization problem using a real-value genetic algorithm by minimizing the objective function, which directly compares the first three natural frequencies changes from the phase-based motion magnification measurement and from the analytical model of a damaged cantilever beam.

scholarly journals Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

2014 ◽  
Vol 6 ◽  
pp. 624949 ◽  
Author(s):  
Kittipong Boonlong
Keyword(s):  
Genetic Algorithm ◽  
Damage Detection ◽  
Cantilever Beam ◽  
Cross Section ◽  
Random Noise ◽  
Vibration Characteristics ◽  
Mode Shapes ◽  
Test Problems ◽  
Uniform Cross Section ◽  
Coevolutionary Genetic Algorithm

Vibration-based damage detection, a nondestructive method, is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage happens. This paper presents cooperative coevolutionary genetic algorithm (CCGA), which is capable for an optimization problem with a large number of decision variables, as the optimizer for the vibration-based damage detection in beams. In the CCGA, a minimized objective function is a numerical indicator of differences between vibration characteristics of the actual damage and those of the anticipated damage. The damage detection in a uniform cross-section cantilever beam, a uniform strength cantilever beam, and a uniform cross-section simply supported beam is used as the test problems. Random noise in the vibration characteristics is also considered in the damage detection. In the simulation analysis, the CCGA provides the superior solutions to those that use standard genetic algorithms presented in previous works, although it uses less numbers of the generated solutions in solution search. The simulation results reveal that the CCGA can efficiently identify the occurred damage in beams for all test problems including the damage detection in a beam with a large number of divided elements such as 300 elements.

FEM Free Damage Detection of Beam Structures Using the Deflections Estimated by Modal Flexibility Matrix

2021 ◽  
pp. 2150128
Author(s):  
Wen-Yu He ◽  
Wei-Xin Ren ◽  
Lei Cao ◽  
Quan Wang
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Index ◽  
Element Model ◽  
Mode Shapes ◽  
Beam Structures ◽  
Flexibility Matrix ◽  
Damage Quantification ◽  
Modal Flexibility ◽  
The Cost

The deflection of the beam estimated from modal flexibility matrix (MFM) indirectly is used in structural damage detection due to the fact that deflection is less sensitive to experimental noise than the element in MFM. However, the requirement for mass-normalized mode shapes (MMSs) with a high spatial resolution and the difficulty in damage quantification restricts the practicability of MFM-based deflection damage detection. A damage detection method using the deflections estimated from MFM is proposed for beam structures. The MMSs of beams are identified by using a parked vehicle. The MFM is then formulated to estimate the positive-bending-inspection-load (PBIL) caused deflection. The change of deflection curvature (CDC) is defined as a damage index to localize damage. The relationship between the damage severity and the deflection curvatures is further investigated and a damage quantification approach is proposed accordingly. Numerical and experimental examples indicated that the presented approach can detect damages with adequate accuracy at the cost of limited number of sensors. No finite element model (FEM) is required during the whole detection process.

scholarly journals A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 112 ◽  
Author(s):  
Ruhua Wang ◽  
Ling Li ◽  
Jun Li
Keyword(s):  
Deep Learning ◽  
Dimension Reduction ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Steel Structure ◽  
Mode Shapes ◽  
Multi Scale ◽  
Training Samples ◽  
Sparsity Constraint ◽  
Deep Learning Network

In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches.

scholarly journals Structural Damage Detection Using Supervised Nonlinear Support Vector Machine

2021 ◽  
Vol 5 (11) ◽  
pp. 303
Author(s):  
Kian K. Sepahvand
Keyword(s):  
Support Vector Machine ◽  
Damage Detection ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Training Data ◽  
Support Vector ◽  
Lightweight Structures ◽  
Straightforward Method ◽  
Classification Boundary ◽  
Nonlinear Support

Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.

VIBRATION-BASED STRUCTURAL DAMAGE DETECTION UNDER VARYING TEMPERATURE CONDITIONS

2013 ◽  
Vol 13 (05) ◽  
pp. 1250082 ◽  
Author(s):  
XIAO-QING ZHOU ◽  
WEN HUANG
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Environmental Changes ◽  
Model Updating ◽  
Modal Testing ◽  
Structural Damage Detection ◽  
Vibration Data ◽  
Temperature Conditions ◽  
Vibration Properties

In vibration-based structural damage detection, it is necessary to discriminate the variation of structural properties due to environmental changes from those caused by structural damages. The present paper aims to investigate the temperature effect on vibration-based structural damage detection in which the vibration data are measured under varying temperature conditions. A simply-supported slab was tested in laboratory to extract the vibration properties with modal testing. The slab was then damaged and the modal testing was conducted again, in which the temperature varied. The modal data measured under different temperature conditions were used to detect the damage with a two-stage model updating technique. Some damage was falsely detected if the temperature variation was not considered. Natural frequencies were then corrected to those under the same temperature conditions according to the relation between the temperature and material modulus. It is shown that all of the damaged elements can be accurately identified.

L1 Regularized Model Updating for Structural Damage Detection

2018 ◽  
Vol 18 (12) ◽  
pp. 1850157 ◽  
Author(s):  
Yu-Han Wu ◽  
Xiao-Qing Zhou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Regularization Parameter ◽  
Structural Vibration ◽  
Mode Shapes ◽  
Modal Data ◽  
Vibration Data ◽  
Stiffness Reduction ◽  
Regularized Model

Model updating methods based on structural vibration data have been developed and applied to detecting structural damages in civil engineering. Compared with the large number of elements in the entire structure of interest, the number of damaged elements which are represented by the stiffness reduction is usually small. However, the widely used [Formula: see text] regularized model updating is unable to detect the sparse feature of the damage in a structure. In this paper, the [Formula: see text] regularized model updating based on the sparse recovery theory is developed to detect structural damage. Two different criteria are considered, namely, the frequencies and the combination of frequencies and mode shapes. In addition, a one-step model updating approach is used in which the measured modal data before and after the occurrence of damage will be compared directly and an accurate analytical model is not needed. A selection method for the [Formula: see text] regularization parameter is also developed. An experimental cantilever beam is used to demonstrate the effectiveness of the proposed method. The results show that the [Formula: see text] regularization approach can be successfully used to detect the sparse damaged elements using the first six modal data, whereas the [Formula: see text] counterpart cannot. The influence of the measurement quantity on the damage detection results is also studied.

scholarly journals Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab

2020 ◽  
Vol 10 (8) ◽  
pp. 2869 ◽  
Author(s):  
Zhenpeng Wang ◽  
Minshui Huang ◽  
Jianfeng Gu
Keyword(s):  
Elastic Modulus ◽  
Reinforced Concrete ◽  
Ambient Temperature ◽  
Temperature Effects ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Mode Shapes ◽  
Arx Model ◽  
Rc Slab

To study the variations in modal properties of a reinforced concrete (RC) slab (such as natural frequencies, mode shapes and damping ratios) under the influence of ambient temperature, a laboratory RC slab is monitored for over a year, the simple linear regression (LR) and autoregressive with exogenous input (ARX) models between temperature and frequencies are established and validated, and a damage identification based on particle swarm optimization (PSO) is utilized to detect the assumed damage considering temperature effects. Firstly, the vibration testing is performed for one year and the variations of natural frequencies, mode shapes and damping ratios under different ambient temperatures are analyzed. The obtained results show that the change of ambient temperature causes a major change of natural frequencies, which, on the contrary, has little effect on damping ratios and modal shapes. Secondly, based on a theoretical derivation analysis of natural frequency, the models are determined from experimental data on the healthy structure, and the functional relationship between temperature and elastic modulus is obtained. Based on the monitoring data, the LR model and ARX model between structural elastic modulus and ambient temperature are acquired, which can be used as the baseline of future damage identification. Finally, the established ARX model is validated based on a PSO algorithm and new data from the assumed 5% uniform damage and 10% uniform damage are compared with the models. If the eigenfrequency exceeds the certain confidence interval of the ARX model, there is probably another cause that drives the eigenfrequency variations, such as structural damage. Based on the constructed ARX model, the assumed damage is identified accurately.

Sign in / Sign up

Export Citation Format

Share Document