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

    Source: Journal of Pressure Vessel Technology:;2020:;volume( 142 ):;issue: 006::page 061601-1
    Author:
    Hu, Chaojie
    ,
    Yang, Bin
    ,
    Yan, Jianjun
    ,
    Xiang, Yanxun
    ,
    Zhou, Shaoping
    ,
    Xuan, Fu-Zhen
    DOI: 10.1115/1.4047213
    Publisher: The American Society of Mechanical Engineers (ASME)
    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.
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      Damage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4275311
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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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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian