文献类型：期刊文献

英文题名：Damage Localization in Pressure Vessel by Guided Waves Based on Convolution Neural Network Approach

作者：Hu, Chaojie[1];Yang, Bin[1];Yan, Jianjun[1];Xiang, Yanxun[1];Zhou, Shaoping[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2020

卷号：142

期号：6

外文期刊名：JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME

收录：;EI(收录号：20210910005604);WOS:【SCI-EXPANDED(收录号:WOS:000591718800013)】；

基金：National Key Technology R&D Program of China (No. 2018YFC0808800; Funder ID: 10.13039/501100013290). National Natural Science Foundation of China (No.11702097, 51835003; Funder ID: 10.13039/501100001809). Fundamental Research Funds for the Central Universities (No. 222201714015; Funder ID: 10.13039/501100012226).

语种：英文

外文关键词：damage localization; convolution neural network; pressure vessel; guided wave

摘要：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.