Damage localization in pressure vessel using guided wave-based techniques: Optimizing the sensor array configuration to mitigate nozzle effects

2022 ◽  
Vol 185 ◽  
pp. 108393
Author(s):  
Chaojie Hu ◽  
Bin Yang ◽  
Biao Xiao ◽  
Fu-Zhen Xuan ◽  
Yanxun Xiang
Keyword(s):  
Pressure Vessel ◽  
Sensor Array ◽  
Guided Wave ◽  
Damage Localization ◽  
Array Configuration

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.

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.

Defect Localization for Pressure Vessel Based on Circumferential Guided Waves: An Experimental Study

2018 ◽  
Author(s):  
Shuangmiao Zhai ◽  
Chaofeng Chen ◽  
Gangyi Hu ◽  
Shaoping Zhou
Keyword(s):  
Pressure Vessel ◽  
Guided Waves ◽  
Pressure Vessels ◽  
Guided Wave ◽  
Normal Operation ◽  
Propagation Characteristics ◽  
Dense Array ◽  
Imaging Algorithm ◽  
Sparse Array ◽  
Damage Imaging

Pressure vessels are normally employed under extreme environments with high temperature and high pressure. Inevitably, the defects like crack and corrosion that easily occur in the equipment and can significantly influence the normal operation. Guided wave-based method is a cost-effective means to measure the utility of pressure vessel. In this paper, finite element (FE) simulation is used to explore the propagation characteristics of circumferential guided waves in pressure vessel. Based on the propagation characteristics, the experiments with different configurations of piezoelectric transducers (PETs), which contain a sparse array and a dense array, have been conducted on pressure vessel respectively. Different imaging methods, including discrete ellipse imaging algorithm and probability damage imaging algorithm have been applied to locate the defect based on the configurations above. Furthermore, a multi-channel ultrasonic guided wave detection system has been set up for pressure vessel inspection. The experimental results show that the sparse array with the discrete ellipse imaging algorithm can locate the defect effectively. The imaging results based on probability damage imaging algorithm show that the dense array presents the better localization result.

A Study on Defect Tomographic Imaging in Pressure Vessel With Liquid Medium

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Gangyi Hu ◽  
Chaofeng Chen ◽  
Shaoping Zhou ◽  
Shuangmiao Zhai
Keyword(s):  
Pressure Vessel ◽  
Reconstruction Algorithm ◽  
Pressure Vessels ◽  
Guided Wave ◽  
Liquid Level ◽  
Matching Method ◽  
Imaging Results ◽  
Guided Wave Propagation ◽  
Financial Consequences ◽  
Tomography Method

Abstract Pressure vessels are widely utilized in many areas of industrial production and daily life for medium storage, which causes performance degradation in pressure vessels, such as crack and corrosion, and lead to serious safety and financial consequences. Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID) is a kind of guided wave-based tomography method which is suitable to evaluate structure integrity of pressure vessels. In this article, the effect of liquid level on guided wave propagation and imaging results of RAPID algorithm is investigated, and an optimal baseline matching method based on amplitude variance is proposed to improve the imaging accuracy of RAPID algorithm with liquid-contained condition. The attenuation effect of liquid on guided wave amplitude is investigated. The damage signals are matched with baseline signals recorded at different liquid levels, and the effect of liquid on RAPID algorithm is discussed based on the results. The experiment of image reconstruction for pressure vessel using the optimal baseline matching method based RAPID algorithm is conducted as well. The experimental results show that the optimal baseline matching method can effectively select the best baseline signal, and the reconstructed images can accurately locate the defects on pressure vessels with considering the change of liquid level.

scholarly journals A Lamb Wave Wavenumber-Searching Method for a Linear PZT Sensor Array

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4166 ◽  
Author(s):  
Bin Liu ◽  
Tingzhang Liu ◽  
Jianfei Zhao
Keyword(s):  
Time Domain ◽  
Lamb Wave ◽  
Sensor Array ◽  
Fiber Composite ◽  
Sampling Rate ◽  
Laminate Plate ◽  
Spatial Sampling ◽  
Damage Localization ◽  
Carbon Fiber Composite ◽  
Searching Method

In this paper, a wavenumber–searching method based on time-domain compensation is proposed to obtain the wavenumber of the Lamb wave array received signal. In the proposed method, the time-domain sampling signal of the linear piezoelectric transducer (PZT) sensor array is converted into a spatial sampling signal using the searching wavenumber. The two–dimensional time-spatial-domain Lamb wave received signal of the linear PZT sensor array is then converted into a one-dimensional synthesized spatial sampling signal. Further, the sum of squared errors between the synthesized spatial sampling signal and its Morlet wavelet fitting signal is calculated at each searching wavenumber. Finally, the wavenumber of the Lamb wave array received signal is obtained as the searching wavenumber corresponding to the minimum error. This method was validated on a 2024-T3 aluminum alloy. The validation results showed that the proposed method can successfully obtain the wavenumber of the Lamb wave array received signal, whose spatial sampling rate does not satisfy the Nyquist sampling theorem; the wavenumber error does not exceed 2.2 rad/m. Damage localization based on the proposed method was also validated on a carbon fiber composite laminate plate, and the maximum damage localization error was no more than 2.11 cm.

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