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contributor authorHu, Chaojie
contributor authorYang, Bin
contributor authorYan, Jianjun
contributor authorXiang, Yanxun
contributor authorZhou, Shaoping
contributor authorXuan, Fu-Zhen
date accessioned2022-02-04T22:18:32Z
date available2022-02-04T22:18:32Z
date copyright6/12/2020 12:00:00 AM
date issued2020
identifier issn0094-9930
identifier otherpvt_142_06_061601.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4275311
description abstractThis 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.
publisherThe American Society of Mechanical Engineers (ASME)
titleDamage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach
typeJournal Paper
journal volume142
journal issue6
journal titleJournal of Pressure Vessel Technology
identifier doi10.1115/1.4047213
journal fristpage061601-1
journal lastpage061601-13
page13
treeJournal of Pressure Vessel Technology:;2020:;volume( 142 ):;issue: 006
contenttypeFulltext


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