Signal-segments cross-coherence method for nonlinear structural damage detection using free-vibration signals

2019 ◽  
Vol 23 (6) ◽  
pp. 1041-1054
Author(s):  
Yingchao Li ◽  
Wei Sun ◽  
Ruinian Jiang ◽  
Yanqing Han
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Decomposition Method ◽  
New Method ◽  
Scale Model ◽  
Noise Robustness ◽  
Offshore Wind Turbine ◽  
Damage Localization ◽  
Localization Index ◽  
Coherence Index

A damage detection process can be significantly enhanced if the nonlinear effects can be used when extracting damage-sensitive features from measured signals. The coherence function is typically used for nonlinearity identification by determining the extent of the output power linearly correlated with the input. However, the excitations are usually difficult to measure in actual tests. To overcome this limit, this article presents a signal-segments cross-coherence method for nonlinearity identification. By defining a signal-segments cross-coherence matrix and signal-segments cross-coherence index, the method can visually and quantitatively indicate the presence of nonlinearity. The innovation of the new method is that the coherence analysis process only depends on a single output signal, where input and baseline signals are not required. Then, a novel structural damage localization index is constructed by multi-point comparing of the signal-segments cross-coherence indices, based on the assumption that all the measured signals from different points on the structure have the same frequency bandwidth and components. To meet this requirement, a newly proposed signal decomposition method called analytic mode decomposition method is adopted. Numerical studies on a duffing oscillator and a 10 degree-of-freedom spring-damping-mass system were performed to demonstrate the nonlinear identification process and investigate the effectiveness and robustness of the signal-segments cross-coherence-based damage detection method. The results show that the signal-segments cross-coherence method can effectively indicate the appearance of nonlinearity by the signal-segments cross-coherence matrix and signal-segments cross-coherence index with strong noise robustness. And the proposed damage localization index can accurately detect the weak nonlinear damage even with severely noise-polluted signals. To further investigate the applicability of the new method, an experimental study was conducted on a steel simplified scale model of a monopile offshore wind turbine support structure. The results demonstrate that the proposed signal-segments cross-coherence method and the new damage localization index can be used to detect the bolt-loosening damage of the steel structure only with output signals.

scholarly journals Structural Damage Detection Using Slopes of Longitudinal Vibration Shapes

2015 ◽  
Author(s):  
W. Xu ◽  
W. D. Zhu ◽  
S. A. Smith
Keyword(s):  
Steady State ◽  
Damage Detection ◽  
Structural Damage ◽  
Longitudinal Vibration ◽  
Harmonic Excitation ◽  
Mode Shapes ◽  
Noise Robustness ◽  
Steady State Response ◽  
Structural Damage Detection ◽  
State Response

While structural damage detection based on flexural vibration shapes, such as mode shapes and steady-state response shapes under harmonic excitation, has been well developed, little attention is paid to that based on longitudinal vibration shapes that also contain damage information. This study originally formulates a slope vibration shape for damage detection in bars using longitudinal vibration shapes. To enhance noise robustness of the method, a slope vibration shape is transformed to a multiscale slope vibration shape in a multiscale domain using wavelet transform, which has explicit physical implication, high damage sensitivity, and noise robustness. These advantages are demonstrated in numerical cases of damaged bars, and results show that multiscale slope vibration shapes can be used for identifying and locating damage in a noisy environment. A three-dimensional (3D) scanning laser vibrometer is used to measure the longitudinal steady-state response shape of an aluminum bar with damage due to reduced cross-sectional dimensions under harmonic excitation, and results show that the method can successfully identify and locate the damage. Slopes of longitudinal vibration shapes are shown to be suitable for damage detection in bars and have potential for applications in noisy environments.

How Many Vibration Response Sensors for Damage Detection & Localization on a Structural Topology? An Experimental Exploratory Study

2013 ◽  
Vol 569-570 ◽  
pp. 791-798
Author(s):  
Christos S. Sakaris ◽  
John S. Sakellariou ◽  
Spilios D. Fassois
Keyword(s):  
Damage Detection ◽  
Exploratory Study ◽  
Structural Damage ◽  
Vibration Response ◽  
Truss Structure ◽  
Damage Localization ◽  
Structural Topology ◽  
Structural Damage Detection

The number of vibration response sensors required for structural damage detection andprecise localization on a continuous structural topology is investigated. For damage detection thestate–of–the–art of vibration based methods need a required number of sensors q that may be “low”compared to the number of structural modes m, that is q << m. Yet, the opposite is generally suggestedfor precise damage localization, that is q > m. In this study the hypothesis that a “low” numberof vibration response sensors, q << m, may, under certain conditions, suffice for precise damage localization,is postulated. This hypothesis is “proven” experimentally by demonstrating that preciselocalization is indeed possible using a single vibration response sensor and an advanced StructuralHealth Monitoring methodology on a laboratory 3D truss structure.

scholarly journals Structural damage detection based on improved frequency change ratio

2019 ◽  
Vol 272 ◽  
pp. 01010
Author(s):  
Jian WANG ◽  
Huan JIN ◽  
Xiao MA ◽  
Bin ZHAO ◽  
Zhi YANG ◽  
...  
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Detection Method ◽  
Mode Shapes ◽  
Frequency Change ◽  
Damage Localization ◽  
Structural Damage Detection ◽  
Practical Application ◽  
Slight Damage

Frequency Change Ratio (FCR) based damage detection methodology for structural health monitoring (SHM) is analyzed in detail. The effectiveness of damage localization using FCR for some slight damage cases and worse ones are studied on an asymmetric planar truss numerically. Disadvantages of damage detection using FCR in practical application are found and the reasons for the cases are discussed. To conquer the disadvantages of FCR, an Improved Frequency Change Ratio (IFCR) based damage detection method which takes the changes of mode shapes into account is proposed. Verification is done in some damage cases and the results reveal that IFCR can identify the damage more efficiently. Noisy cases are considered to assess the robustness of IFCR and results indicate that the proposed method can work well when the noise is not severe.

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.

A Frequency Response Based Structural Damage Localization Method Using Proper Orthogonal Decomposition

2011 ◽  
Vol 27 (2) ◽  
pp. 157-166 ◽  
Author(s):  
M. Salehi ◽  
S. Ziaei-Rad ◽  
M. Ghayour ◽  
M.A. Vaziri-Zanjani
Keyword(s):  
Damage Detection ◽  
Frequency Response ◽  
Proper Orthogonal Decomposition ◽  
Structural Damage ◽  
Orthogonal Decomposition ◽  
Damage Localization ◽  
Response Functions ◽  
Structural Damage Detection ◽  
Harmonic Response ◽  
Proper Orthogonal

ABSTRACTVibration-based structural damage detection has been the focus of attention by many researchers over the last few decades. However, most methods proposed for this purpose utilize extracted modal parameters or some indices constructed based on these parameters. A literature review revealed that few papers have employed Frequency Response Functions (FRFs) for detecting structural damage. In this paper, a technique is presented for damage detection which is based on measured FRFs. Proper Orthogonal Decomposition (POD) has been implemented on spatiotemporal responses in each frequency in order to reduce the dimension of the data. This is based on the concept that the forced harmonic response of a linear vibrating system can be fully captured utilizing a single basis vector. A different approach is also presented in this paper in which POD is applied to the frequency domain data. Operational Deflection Shapes (ODSs) have been decomposed using POD to localize the damage. The efficiency of the method is demonstrated through some numerical and experimental case studies.

scholarly journals Structural Damage Detection Using Slopes of Longitudinal Vibration Shapes

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
W. Xu ◽  
W. D. Zhu ◽  
S. A. Smith ◽  
M. S. Cao
Keyword(s):  
Steady State ◽  
Damage Detection ◽  
Structural Damage ◽  
Longitudinal Vibration ◽  
Harmonic Excitation ◽  
Mode Shapes ◽  
Noise Robustness ◽  
Steady State Response ◽  
Structural Damage Detection ◽  
State Response

While structural damage detection based on flexural vibration shapes, such as mode shapes and steady-state response shapes under harmonic excitation, has been well developed, little attention is paid to that based on longitudinal vibration shapes that also contain damage information. This study originally formulates a slope vibration shape (SVS) for damage detection in bars using longitudinal vibration shapes. To enhance noise robustness of the method, an SVS is transformed to a multiscale slope vibration shape (MSVS) in a multiscale domain using wavelet transform, which has explicit physical implication, high damage sensitivity, and noise robustness. These advantages are demonstrated in numerical cases of damaged bars, and results show that MSVSs can be used for identifying and locating damage in a noisy environment. A three-dimensional (3D) scanning laser vibrometer (SLV) is used to measure the longitudinal steady-state response shape of an aluminum bar with damage due to reduced cross-sectional dimensions under harmonic excitation, and results show that the method can successfully identify and locate the damage. Slopes of longitudinal vibration shapes are shown to be suitable for damage detection in bars and have potential for applications in noisy environments.

Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm

2017 ◽  
Vol 17 (2) ◽  
pp. 313-324 ◽  
Author(s):  
Young-Jin Cha ◽  
Zilong Wang
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Clustering Algorithm ◽  
Novelty Detection ◽  
Damage Localization ◽  
Damage Scenarios ◽  
Density Peaks ◽  
Testing Data ◽  
Detection And Localization

Within machine learning, several structural damage detection and localization methods based on clustering and novelty detection methods have been proposed in the recent years in order to monitor mechanical and civil structures. In order to train a machine learning model, an unsupervised mode is preferred because it only requires sufficient normal data from the intact states of a structure for training, and the testing abnormal data from various damage states are generally quite rare. With an unsupervised training mode, the capability of detecting structural damage mainly depends on the identification of abnormal data from the testing data. This identification process is termed unsupervised novelty detection. The premise of unsupervised novelty detection is that a large volume of a normal data set is available first to train a normal model that is established by machine learning algorithms. Then, the trained normal model can be used to identify abnormal data from future testing data. In this article, a new structural damage detection and localization method is proposed using a density peaks-based fast clustering algorithm. In order to realize damage detection, the original density peaks-based fast clustering algorithm is modified to an unsupervised machine learning method by adding training and testing processes. Furthermore, to improve the performance of the proposed method, the Gaussian kernel function of radius is introduced to calculate the local density of data points, and a new damage-sensitive feature using a continuous wavelet transform is also proposed. Damage-sensitive features are extracted from the measured data through sensors installed on a laboratory-scale steel structure. Extensive experimental studies are carried out under various structural damage scenarios in order to validate the performance of the proposed method. The proposed density peaks-based fast clustering method shows satisfactory performance with regard to damage localization under various damage scenarios as compared to a traditional approach.

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