A robust baseline removal method for guided wave damage localization

2014 ◽  
Author(s):  
Chang Liu ◽  
Joel B. Harley ◽  
Mario Bergés ◽  
David W. Greve ◽  
Warren R. Junker ◽  
...  
Keyword(s):  
Guided Wave ◽  
Damage Localization ◽  
Removal Method ◽  
Baseline Removal

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.

Damage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Chaojie Hu ◽  
Bin Yang ◽  
Jianjun Yan ◽  
Yanxun Xiang ◽  
Shaoping Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Pressure Vessel ◽  
Guided Waves ◽  
Pressure Vessels ◽  
Guided Wave ◽  
Damage Localization ◽  
Accuracy Rate ◽  
Neural Network Approach ◽  
Monitoring Process ◽  
Fully Connected

Abstract This paper investigates the damage localization in a pressure vessel using guided wave-based structural health monitoring (SHM) technology. An online SHM system was developed to automatically select the guided wave propagating path and collect the generated signals during the monitoring process. Deep learning approach was employed to train the convolutional neural network (CNN) model by the guided wave datasets. Two piezo-electric ceramic transducers (PZT) arrays were designed to verify the anti-interference ability and robustness of the CNN model. Results indicate that the CNN model with seven convolution layers, three pooling layers, one fully connected layer, and one Softmax layer could locate the damage with 100% accuracy rate without overfitting. This method has good anti-interference ability in vibration or PZTs failure condition, and the anti-interference ability increases with increasing of PZT numbers. The trained CNN model can locate damage with high accuracy, and it has great potential to be applied in damage localization of pressure vessels.

A Model-Free, Fully Automated Baseline-Removal Method for Raman Spectra

2011 ◽  
Vol 65 (1) ◽  
pp. 75-84 ◽  
Author(s):  
H. Georg Schulze ◽  
Rod B. Foist ◽  
Kadek Okuda ◽  
André Ivanov ◽  
Robin F. B. Turner
Keyword(s):  
Raman Spectra ◽  
Removal Method ◽  
Model Free ◽  
Baseline Removal
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