Microscopic and Mesoscopic Damage Localization

2009 ◽  
pp. 119-119-12
Author(s):  
YH Agha ◽  
F Hild ◽  
R Billardon
Keyword(s):  
Damage Localization

Probability-based diagnostic imaging with corrected weight distribution for damage detection of stiffened composite panel

2021 ◽  
pp. 147592172110339
Author(s):  
Guoqiang Liu ◽  
Binwen Wang ◽  
Li Wang ◽  
Yu Yang ◽  
Xiaguang Wang
Keyword(s):  
Distribution Function ◽  
Diagnostic Imaging ◽  
Weight Distribution ◽  
Composite Structures ◽  
Damage Identification ◽  
Guided Wave ◽  
Composite Panel ◽  
Damage Localization ◽  
Localization Accuracy ◽  
Direct Interpretation

Due to no requirement for direct interpretation of the guided wave signal, probability-based diagnostic imaging (PDI) algorithm is especially suitable for damage identification of complex composite structures. However, the weight distribution function of PDI algorithm is relatively inaccurate. It can reduce the damage localization accuracy. In order to improve the damage localization accuracy, an improved PDI algorithm is proposed. In the proposed algorithm, the weight distribution function is corrected by the acquired relative distances from defects to all actuator–sensor pairs and the reduction of the weight distribution areas. The validity of the proposed algorithm is assessed by identifying damages at different locations on a stiffened composite panel. The results show that the proposed algorithm can identify damage of a stiffened composite panel accurately.

scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

scholarly journals Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 180
Author(s):  
Lei Fu ◽  
Qizhi Tang ◽  
Peng Gao ◽  
Jingzhou Xin ◽  
Jianting Zhou
Keyword(s):  
Short Term Memory ◽  
Damage Identification ◽  
Identification Accuracy ◽  
Damage Localization ◽  
Short Term ◽  
Term Memory ◽  
Localization Accuracy ◽  
Long Span Bridges ◽  
Long Span ◽  
Long Short Term Memory

The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.

Uncertainty Quantification for Stochastic Damage Localization for Mechanical Systems*

2012 ◽  
Vol 45 (20) ◽  
pp. 1017-1022 ◽  
Author(s):  
Luciano Marin ◽  
Michael Döhler ◽  
Dionisio Bernal ◽  
Laurent Mevel
Keyword(s):  
Uncertainty Quantification ◽  
Mechanical Systems ◽  
Damage Localization ◽  
Stochastic Damage

Damage localization in structures without baseline modal parameters

AIAA Journal ◽  
1996 ◽  
Vol 34 (8) ◽  
pp. 1644-1649 ◽  
Author(s):  
Norris Stubbs ◽  
Jeong-Tae Kim
Keyword(s):  
Modal Parameters ◽  
Damage Localization
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