A deep learning database and network for focusing guided wave defect detection

Citation Author(s):
Hongyu
Sun
Submitted by:
Hongyu Sun
Last updated:
Fri, 07/10/2020 - 03:51
DOI:
ERROR: error: The attribute 'style' is not allowed. at line 28, column 0
Data Format:
License:
119 Views
Categories:
Keywords:
0
0 ratings - Please login to submit your rating.

Abstract 

Abstract: 

Database set information

Instructions: 

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.

s