期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 70, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3091204
关键词
Convolutional neural network; deep learning; guided wave; probability imaging; structural health monitoring
资金
- Guangdong Province Key Research and Development Program [2020B0404010001]
- National Natural Science Foundation of China [51975220]
- Guangdong Outstanding Youth Fund [2019B151502057]
- Fundamental Research Funds for Central Universities Project [2019ZD23]
The proposed deep convolutional neural network probability imaging algorithm provides an automatic high-level damage index extraction method for guided wave imaging, overcoming the limitations of manual feature extraction and showing good generalization and performance in detecting damage.
Guided wave imaging can obtain the damage characterization of imaging features and time difference, and has become a promising tool for structural health monitoring. But most current imaging algorithms still manually select imaging features from first wave or scatter signal. Such manual feature extraction method highly depends on the selection criterion and would significantly reduce the generalization of the monitoring model. In this article, a deep convolutional neural network probability imaging algorithm (DCNN-PIA) is proposed to provide an automatic high-level damage index extraction method for guided wave imaging. Feature selection and multisensor imbalance can be turned away in this semisupervised deep learning method, and the damage will be presented visually. The experiment results illustrate that the proposed method can detect the damage only by using normal state signals, presents a good materials generalization in both aluminum plate and composite plate, and has better performance than other state-of-the-art methods.
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