Elimination of outlier measurements for damage detection of structures under changing environmental conditions

2021 ◽  
pp. 147592172199847
Author(s):  
William Soo Lon Wah ◽  
Yining Xia
Keyword(s):  
Damage Detection ◽  
Multiple Regression ◽  
Natural Frequencies ◽  
Detection Method ◽  
Detection Methods ◽  
Structure Model ◽  
Bridge Structure ◽  
Beam Structure ◽  
Dependent Variables ◽  
The Difference

Damage detection methods developed in the literature are affected by the presence of outlier measurements. These measurements can prevent small levels of damage to be detected. Therefore, a method to eliminate the effects of outlier measurements is proposed in this article. The method uses the difference in fits to examine how deleting an observation affects the predicted value of a model. This allows the observations that have a large influence on the model created, to be identified. These observations are the outlier measurements and they are eliminated from the database before the application of damage detection methods. Eliminating the outliers before the application of damage detection methods allows the normal procedures to detect damage, to be implemented. A multiple-regression-based damage detection method, which uses the natural frequencies as both the independent and dependent variables, is also developed in this article. A beam structure model and an experimental wooden bridge structure are analysed using the multiple-regression-based damage detection method with and without the application of the method proposed to eliminate the effects of outliers. The results obtained demonstrate that smaller levels of damage can be detected when the effects of outlier measurements are eliminated using the method proposed in this article.

scholarly journals Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation

2021 ◽  
Vol 11 (10) ◽  
pp. 4589
Author(s):  
Ivan Duvnjak ◽  
Domagoj Damjanović ◽  
Marko Bartolac ◽  
Ana Skender
Keyword(s):  
Boundary Conditions ◽  
Damage Detection ◽  
Mode Shape ◽  
Experimental Validation ◽  
Detection Method ◽  
Dynamic Properties ◽  
Damage Index ◽  
Main Principle ◽  
The Difference ◽  
Different Levels

The main principle of vibration-based damage detection in structures is to interpret the changes in dynamic properties of the structure as indicators of damage. In this study, the mode shape damage index (MSDI) method was used to identify discrete damages in plate-like structures. This damage index is based on the difference between modified modal displacements in the undamaged and damaged state of the structure. In order to assess the advantages and limitations of the proposed algorithm, we performed experimental modal analysis on a reinforced concrete (RC) plate under 10 different damage cases. The MSDI values were calculated through considering single and/or multiple damage locations, different levels of damage, and boundary conditions. The experimental results confirmed that the MSDI method can be used to detect the existence of damage, identify single and/or multiple damage locations, and estimate damage severity in the case of single discrete damage.

scholarly journals A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

2019 ◽  
Vol 9 (13) ◽  
pp. 2771 ◽  
Author(s):  
Ping Zhou ◽  
Gongbo Zhou ◽  
Zhencai Zhu ◽  
Zhenzhi He ◽  
Xin Ding ◽  
...  
Keyword(s):  
Damage Detection ◽  
Detection Method ◽  
Steel Wire ◽  
Guided Wave ◽  
Detection Methods ◽  
Wire Ropes ◽  
Intelligent Sensors ◽  
Detection Technology ◽  
Damage States ◽  
Non Destructive

As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.

Structural Health Monitoring Using Synchrosqueezed Wavelet Transform on IASC-ASCE Benchmark Phase I

2020 ◽  
Vol 20 (12) ◽  
pp. 2050138
Author(s):  
Wilson D. Sanchez ◽  
Jose V. de Brito ◽  
Suzana M. Avila
Keyword(s):  
Wavelet Transform ◽  
Dynamic Response ◽  
Phase I ◽  
Damage Detection ◽  
Health Monitoring ◽  
Natural Frequencies ◽  
Detection Methods ◽  
State Of Health ◽  
Civil Structures ◽  
Synchrosqueezed Wavelet Transform

Civil structures suffer deterioration either for years of service, deficiency due to environmental factors or damages caused by factors such as earthquakes, winds, impact loads, and cyclical loads. When a structure ages, it is necessary to know its state of health and make a decision of maintenance or replacement. When a structure such as a bridge or building is subjected to destructive environmental forces, determining its state of health becomes a priority since its recovery is urgently required to function normally. Structural Health Monitoring (SHM) is a technology that aims to prevent the collapse of structures and loss of human life through early diagnosis of the health status of a structure. There are a large number of damage detection methods that can be classified into (1) non-destructive testing methods, (2) dynamic characteristics-based damage detection methods, (3) dynamic response-based, (4) multi-scale damage detection method and (5) damage detection methods with consideration of uncertainties. In this work, it is implemented synchrosqueezed wavelet transform (SWT), which can be classified as a methods based on the dynamic response. To validate the robustness of the method it is identified first, the natural frequencies of the Benchmark Phase I without damage, which consists of a steel structure of 4-story [Formula: see text] bay 3D steel frame structure subjected to ambient vibrations. Subsequently, some damage patterns are validated according to IASC-ASCE SHM Task Group. The results obtained in the identification of natural frequencies are compared with those reported in literature. SWT was efficient, presenting a minimum error of 0.12[Formula: see text] and a maximum of 3.06[Formula: see text] in the identification of natural frequencies about the AISCE-ASCE group model. SWT overcomes some other damage detection methods, which are deficient in the identification of closely spaced frequencies, commonly present in many civil structures due to symmetric geometry or similar physical properties in different directions.

Detecting Loosening of Bolted Connections in a Pipeline Using Changes in Natural Frequencies

2011 ◽  
Author(s):  
K. He ◽  
W. D. Zhu
Keyword(s):  
Damage Detection ◽  
Natural Frequencies ◽  
Detection Method ◽  
Complex Geometry ◽  
Early Stage ◽  
Measurement Noise ◽  
Modeling Error ◽  
Bolted Connections ◽  
Bolted Connection ◽  
Stiffness Reduction

Loosening of bolted connections in a structure can significantly reduce the load-bearing capacities of the structure. Detecting loosening of bolted connections at an early stage can avoid failure of the structure. Due to the complex geometry of a bolted connection and the material discontinuity between the clamped components, it is difficult to detect loosening of a bolted connection using conventional non-destructive test methods. A vibration-based method that uses changes in natural frequencies of a structure to detect the locations and extent of damage can be used to detect loosening of bolted connections, since the method focuses on detecting a stiffness reduction, which can result from loosening of the bolted connections. Experimental and numerical damage detection using the vibration-based method was conducted to detect the loosening of the bolted connections in a fullsize steel pipeline with bolted flanges. With the recent development of a predictive modeling technique for bolted connections in thin-walled structures, an accurate physics-based finite element model of the pipeline that is required by the vibration-based damage detection method is developed. A trust-region search strategy is employed to improve the damage detection method so that convergence of the damage detection algorithm can be ensured for under-determined systems, and the robustness of the algorithm can be enhanced when relatively large modeling error and measurement noise are present. The location and extent of the loosened bolted connections were successfully detected in experimental damage detection using changes in the natural frequencies of the first several modes; the exact location and extent of the loosened bolted connections can be detected in the numerical simulation where there are no modeling error and measurement noise.

Structural damage detection via combining weighted strategy with trace Lasso

2018 ◽  
Vol 22 (3) ◽  
pp. 597-612 ◽  
Author(s):  
Chengbin Chen ◽  
Chudong Pan ◽  
Zepeng Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Swarm Intelligence ◽  
Structural Damage ◽  
Optimization Problem ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Structural Damage Detection ◽  
Selection Operator

With the rapid development of computation technologies, swarm intelligence–based algorithms become an innovative technique used for addressing structural damage detection issues, but traditional swarm intelligence–based structural damage detection methods often face with insufficient detection accuracy and lower robustness to noise. As an exploring attempt, a novel structural damage detection method is proposed to tackle the above deficiency via combining weighted strategy with trace least absolute shrinkage and selection operator (Lasso). First, an objective function is defined for the structural damage detection optimization problem by using structural modal parameters; a weighted strategy and the trace Lasso are also involved into the objection function. A novel antlion optimizer algorithm is then employed as a solution solver to the structural damage detection optimization problem. To assess the capability of the proposed structural damage detection method, two numerical simulations and a series of laboratory experiments are performed, and a comparative study on effects of different parameters, such as weighted coefficients, regularization parameters and damage patterns, on the proposed structural damage detection methods are also carried out. Illustrated results show that the proposed structural damage detection method via combining weighted strategy with trace Lasso is able to accurately locate structural damages and quantify damage severities of structures.

scholarly journals Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt

2021 ◽  
Vol 11 (16) ◽  
pp. 7282
Author(s):  
Mengchao Zhang ◽  
Yuan Zhang ◽  
Manshan Zhou ◽  
Kai Jiang ◽  
Hao Shi ◽  
...  
Keyword(s):  
Damage Detection ◽  
Working Conditions ◽  
Detection Method ◽  
Conveyor Belt ◽  
Detection Methods ◽  
Test Accuracy ◽  
Test Results ◽  
Test Speed ◽  
Conveyor Belts ◽  
Belt Conveyors

Aiming at the problem that mining conveyor belts are easily damaged under severe working conditions, the paper proposed a deep learning-based conveyor belt damage detection method. To further explore the possibility of the application of lightweight CNNs in the detection of conveyor belt damage, the paper deeply integrates the MobileNet and Yolov4 network to achieve the lightweight of Yolov4, and performs a test on the exiting conveyor belt damage dataset containing 3000 images. The test results show that the lightweight network can effectively detect the damage of the conveyor belt, with the fastest test speed 70.26 FPS, and the highest test accuracy 93.22%. Compared with the original Yolov4, the accuracy increased by 3.5% with the speed increased by 188%. By comparing other existing detection methods, the strong generalization ability of the model is verified, which provides technical support and empirical reference for the visual monitoring and intelligent development of belt conveyors.

Detecting Loosening of Bolted Connections in a Pipeline Using Changes in Natural Frequencies

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
K. He ◽  
W. D. Zhu
Keyword(s):  
Damage Detection ◽  
Natural Frequencies ◽  
Detection Method ◽  
Complex Geometry ◽  
Early Stage ◽  
Measurement Noise ◽  
Modeling Error ◽  
Bolted Connections ◽  
Bolted Connection ◽  
Stiffness Reduction

Loosening of bolted connections in a structure can significantly reduce its load-bearing capacity. Detecting loosening of bolted connections at an early stage can prevent failure of the structure. Due to the complex geometry of a bolted connection and material discontinuity between clamped components, it is difficult to detect loosening of a bolted connection using conventional nondestructive test methods. A vibration-based method that uses changes in natural frequencies of a structure to detect locations and extent of damage can be used to detect loosening of bolted connections since the method focuses on detecting a stiffness reduction, which can result from loosening of bolted connections. Experimental and numerical damage detection was conducted to detect loosening of bolted connections in a full-size steel pipeline with bolted flanges using the vibration-based method. With the recent development of a modeling technique for bolted connections in thin-walled structures, an accurate physics-based finite element model of the pipeline that is required by the vibration-based damage detection method is developed. A trust-region search strategy is employed to improve the damage detection method so that global convergence of the damage detection algorithm can be ensured for underdetermined systems, and robustness of the algorithm can be enhanced when relatively large modeling error and measurement noise are present. The location and extent of loosened bolted connections were successfully detected in experimental damage detection using changes in natural frequencies of the first several elastic modes of the pipeline; the exact location and extent of the loosened bolted connections can be detected in numerical simulation where there are no modeling error and measurement noise.

A Damage Detection Method: CMSE Combined with Niche GA

2007 ◽  
Vol 334-335 ◽  
pp. 929-932 ◽  
Author(s):  
Xu Ge ◽  
Yun Ju Yan ◽  
Huan Guo Chen
Keyword(s):  
Composite Material ◽  
Damage Detection ◽  
Composite Structures ◽  
Structural Damage ◽  
Strain Energy ◽  
Detection Method ◽  
Detection Methods ◽  
Damage Localization ◽  
Complex Structures ◽  
Modal Strain Energy

The paper presents an effective damage detection method of complex composite structures. It can be carried out through the experimental modal analysis of the damaged structure. The method using the improved Cross Modal Strain Energy (CMSE) technique and Niche GA has many advantages compared with other damage detection methods. The CMSE method can use any modes of the structure and the modes don’t need to be normalized or consistent in scale. The Niche GA improves the efficiency of the calculation and enhances the capacity of identifying structural damage localization. The model is the composite material airfoil case. The numerical results show that the method proposed in this paper is successful for damage detection of complex structures.

scholarly journals Damage Location Index of Shear Structures Based on Changes in First Two Natural Frequencies

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hien HoThu ◽  
Akira Mita
Keyword(s):  
System Identification ◽  
Damage Detection ◽  
Natural Frequencies ◽  
Detection Method ◽  
Identification Methods ◽  
Damage Location ◽  
Shear Structures ◽  
Stiffness Reduction ◽  
Shear Building

A method of detecting the location of damage in shear structures by using only the changes in first two natural frequencies of the translational modes is proposed. This damage detection method can determine the damage location in a shear building by using a Damage Location Index (DLI) based on two natural frequencies for undamaged and damaged states. In this study, damage is assumed to be represented by the reduction in stiffness. This stiffness reduction results in a change in natural frequencies. The uncertainty associated with system identification methods for obtaining natural frequencies is also carefully considered. Some simulations and experiments on shear structures were conducted to verify the performance of the proposed method.

Vibration-Based Damage Detection in Structural Members Utilizing Finite Elements and Evolutionary Algorithms

2006 ◽  
Author(s):  
Amir Poursamad
Keyword(s):  
Finite Elements ◽  
Damage Detection ◽  
Natural Frequencies ◽  
Mode Shapes ◽  
Fe Model ◽  
Structural Members ◽  
Vibration Data ◽  
Detection Approach ◽  
The Difference ◽  
Extent Of Damage

Presented within this paper is the application of finite elements method combined with an evolutionary algorithm to the problem of damage detection in structural members using vibration data. The objective is to identify the position of the damage in structure, and to estimate the extent of the damage. To describe the damage, finite element method (FEM) is used here and the damage is modeled as a reduction in the stiffness of the associated element. Using this model, the effect of damage on the vibration characteristics of the structure is studied. The problem of damage detection is then formulated as an optimization problem. The decision variables are the position of damaged element and the extent of damage. The objective function is considered as the difference between measured natural frequencies and those obtained from FE model of the structure. Only natural frequencies are adopted here, because the measurement of mode shapes is usually accompanied by larger amount of error. The proposed damage detection approach is verified and assessed using a simulated cantilever Euler-Bernoulli beam.

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