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 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.

Identification of multi-defects in an arched composite structure by the corrected probabilistic diagnostic imaging with the fused damage index

2021 ◽  
pp. 1045389X2110322
Author(s):  
Hashen Jin ◽  
Jun Li ◽  
Weibin Li ◽  
Xinlin Qing
Keyword(s):  
Diagnostic Imaging ◽  
Composite Structure ◽  
Shape Factor ◽  
Composite Structures ◽  
Damage Index ◽  
Guided Wave ◽  
Identification Accuracy ◽  
Size Information ◽  
Damage Indices ◽  
Ultrasonic Guided Wave

Due to the complicacy of geometry and structure in the arched composite structure, it is difficult to monitor various kinds of defects accurately. The developed damage probabilistic diagnostic imaging approach based on ultrasonic guided wave energy signal characteristics is very feasible for the structural health monitoring in the arched composite structures. However, the conventional probabilistic diagnostic imaging (PDI) approaches united with the signal energy damage indices ( DIs) have some limitations in the identification of the number, location and specific size information of multi-defects. Thus, the damage shape factor from the single damage-impaired path imminently demands to be majorized to raise the precision and stability of PDI approach in the damage recognition. A corrected probabilistic diagnostic imaging (CPDI) approach integrated with the damage shape factor [Formula: see text] needs to be recommended to precisely inspect the expansion of defect zones and different multi-defects in the arched composite structure. The availability and feasibility of the proposed methods has been validated by the experiments in the tested specimen. The results show that the fused frequency-domain energy DIs can be applied to indicate the expansion of defect zones quantitatively. It is proved that the defect identification accuracy of multi-defects from the CPDI approach can be improved by the majorization of damage shape factor, effectively. It is also clearly observed that the number, location and specific size information of different conditions of multi-defects can be distinguished by using the CPDI algorithm, availably.

Damage Localization in Complex Composite Panels Using Guided Wave Based Structural Health Monitoring System

2011 ◽  
Author(s):  
Yingtao Liu ◽  
Seung Bum Kim ◽  
Aditi Chattopadhyay ◽  
Derek Doyle
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Composite Structures ◽  
Composite Plates ◽  
Guided Wave ◽  
Damage Localization ◽  
Composite Panels ◽  
Time Frequency ◽  
Structural Health ◽  
Damage Location

Knowledge of the damage location in composite structures is a necessary output for both Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM). Although several damage localization approaches using a triangulation method and Time-of-Flight (ToF) of guided waves have been reported in literature, the damage localization technique is still not mature for composite structures with complex material properties, varying thickness and complex geometries. This paper investigates the development of a new approach for SHM and damage localization using a guided wave based active sensing system. In contrast to the traditional ellipse method, the proposed method does not require the information of structural thickness, ToF, or the estimation of group velocities of each guided wave mode at different propagation angles, which is one of the main limitations of most current ToF methodologies involving composites. This approach uses time-frequency analysis to calculate the difference of the ToF of the converted modes for each sensor signal. The damage location and the group velocity are obtained by solving a set of nonlinear equations. The proposed method can be used for composite structures with unknown lay-up and thickness. To validate the proposed method, experiments were conducted on both composite plates and stiffened composite panels. Eight piezoelectric (PZT) transducers were surface-bonded on each composite specimen and used in four pairs. The PZT transducers in each pair were bonded close to each other. In the PZT array, one PZT transducer from one PZT pair was used as the actuator and the other three pairs were used as sensors. A windowed cosine signal was used as the excitation signal. The locations of the delaminations in the composite specimens were validated using a flash thermography system. The accuracy of the proposed method in localizing delaminations was examined through comparison with the experimental measurements.

Non-Destructive Testing of Thermoplastic Carbon Composite Structures

2020 ◽  
Vol 2020 (1) ◽  
pp. 34-52
Author(s):  
Rafał Szymański
Keyword(s):  
Composite Structures ◽  
Ultrasonic Method ◽  
Composite Panel ◽  
Aviation Industry ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulse Technique ◽  
Thermoplastic Matrix ◽  
The Subject ◽  
Non Destructive

AbstractThe article is in line with the contemporary interests of companies from the aviation industry. It describes thermoplastic material and inspection techniques used in leading aviation companies. The subject matter of non-destructive testing currently used in aircraft inspections of composite structures is approximated and each of the methods used is briefly described. The characteristics of carbon preimpregnates in thermoplastic matrix are also presented, as well as types of thermoplastic materials and examples of their application in surface ship construction. The advantages, disadvantages and limitations for these materials are listed. The focus was put on the explanation of the ultrasonic method, which is the most commonly used method during the inspection of composite structures at the production and exploitation stage. Describing the ultrasonic method, the focus was put on echo pulse technique and the use of modern Phased Array heads. Incompatibilities most frequently occurring and detected in composite materials with thermosetting and thermoplastic matrix were listed and described. A thermoplastic flat composite panel made of carbon pre-impregnate in a high-temperature matrix (over 300°C), which was the subject of the study, was described. The results of non-destructive testing (ultrasonic method) of thermoplastic panel were presented and conclusions were drawn.

scholarly journals Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2005
Author(s):  
Veronika Scholz ◽  
Peter Winkler ◽  
Andreas Hornig ◽  
Maik Gude ◽  
Angelos Filippatos
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Damage Identification ◽  
Vibration Response ◽  
Damage Initiation ◽  
Matrix Cracks ◽  
Operational Life ◽  
Multiple Data Sets ◽  
Fully Connected

Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.

Sign in / Sign up

Export Citation Format

Share Document