Defects Classification of Steel Tube Based on Spectrogram and CNN Using Magnetic Flux Leakage Signals-Reference-Cited by-同舟云学术

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Defects Classification of Steel Tube Based on Spectrogram and CNN Using Magnetic Flux Leakage Signals

Published:2023-05-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)
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Author:

Chen Bin¹

Affiliation:

1. School of Information Technology, Anhui Vocational College of City Management,Hefei,China,230011

Funder

Anhui Provincial Department of Education

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10164832/10164812/10165124.pdf?arnumber=10165124

Reference10 articles.

1. Evaluation of inverse algorithms in the analysis of magnetic flux leakage data

2. Quantitative testing of magnetic flux leakage based on BP neural networks;changlong;Ordnance Material Science and Engineering,2007

3. Analysis of MFL signal characteristics using physical field;yanhua;Journal of Huazhong Uinversity of Science and Technology(Natural Science Edition),2010

4. Focused local learning with wavelet neural networks

5. Study on Quantitative Recognition Technology of Pipeline Defect;taiyong;Journal of Tianjin University,2003

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A surface defect detection method for steel pipe based on improved YOLO;Mathematical Biosciences and Engineering;2024

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