Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach

被引：11

作者：

Minhhuy Le ^{[1
,2
]}

Van Su Luong ^{[1
,2
]}

Dang Khoa Nguyen ^{[1
,2
]}

Dang-Khanh Le ^{[3
]}

Lee, Jinyi ^{[4
,5
,6
]}

机构：

[1] Phenikaa Univ, Fac Elect & Elect Engn, Hanoi 12116, Vietnam

[2] Phenikaa Univ, Intelligent Commun Syst Lab ICSLab, Hanoi 12116, Vietnam

[3] Vietnam Maritime Univ, Fac Marine Engn, Haiphong 180000, Vietnam

[4] Chosun Univ, IT Based Real Time NDT Ctr, Gwangju 61452, South Korea

[5] Chosun Univ, Dept Elect Engn, Gwangju 61452, South Korea

[6] Chosun Univ, Interdisciplinary Program It Bio Convergence Syst, Gwangju 61452, South Korea

来源：

APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 10期

关键词：

electromagnetic testing; rivet corrosion; aircraft intake; hall sensor array; machine learning; MULTILAYER CONDUCTIVE STRUCTURES; EDDY-CURRENT DETECTION; FATIGUE CRACKS; INSPECTION; DAMAGE;

D O I：

10.3390/app12105175

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

An aircraft is a multilayer structure that is assembled by rivets. Under extreme working conditions, corrosion appears and quickly propagates at the rivet sites of the layers; thus, it threads the integrity and safety of the aircraft. Corrosion usually occurs at the hidden layer around the rivet, making it difficult to detect. This paper proposes a machine learning approach incorporating an electromagnetic testing system to detect the hidden corrosion at the riveting site effectively. Several machine learning methods will be investigated for the detection of different sizes and locations of corrosion. The training strategy of the machine-learning models on the small numbers of data will also be investigated. The result shows that the proposed approach could effectively detect 89.48% of the hidden corrosion having from 2.8 to 195.4 mm(3) with only 20% of training data and could be increased to 99.0% with 60-80% of the training data.

引用

页数：16