A deep learning database and network for focusing guided wave defect detection
- Citation Author(s):
- Submitted by:
- Hongyu Sun
- Last updated:
- Fri, 07/10/2020 - 03:51
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Database set information
A deep learning database and network for focusing guided wave defect detectionSince the paper is being submitted, the database set will be published after the paper is accepted.Database set information:The defects are classified as three types and specimens with no defect are also included.In the established database set, the defect depth ranges from 10% to 50%, with 10% intervals.In addition, the radius of the pinhole defect ranges from 0.5 mm to 3 mm,and the sizes of the crack defect range from 1×5 mm2 to 2×10 mm2,and the sizes of the corrosion defect range from 5×5 mm2 to 10×10 mm2.Each defect contains 1500 signal data, and the ratio of the training,validation, and test data sets are divided into 6:2:2 in this work. The data storage format andexplain the descriptive data (take the pinhole defect signal with a radius of 3 mm and a depth of 10% as an example).The data set has a total of 48,060,000 signal value data and contains detailed information about defects.
Development environment：TensorFlow 2.2CUDA 10.1Python 3.7
NOTICE: Reviewers can obtain the data set password from the end of the paper's Abstract to run the code.
- Reviewers can obtain the data set password from the end of the paper's abstract to run the code. Data.zip (13.27 MB)