Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach
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, ma...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/10/5175 |
| _version_ | 1797501695787270144 |
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| author | Minhhuy Le Van Su Luong Dang Khoa Nguyen Dang-Khanh Le Jinyi Lee |
| author_facet | Minhhuy Le Van Su Luong Dang Khoa Nguyen Dang-Khanh Le Jinyi Lee |
| author_sort | Minhhuy Le |
| collection | DOAJ |
| description | 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<sup>3</sup> with only 20% of training data and could be increased to 99.0% with 60–80% of the training data. |
| first_indexed | 2024-03-10T03:22:16Z |
| format | Article |
| id | doaj.art-3e17d6ae50dc43978394a61963c89439 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:22:16Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3e17d6ae50dc43978394a61963c894392023-11-23T09:58:48ZengMDPI AGApplied Sciences2076-34172022-05-011210517510.3390/app12105175Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning ApproachMinhhuy Le0Van Su Luong1Dang Khoa Nguyen2Dang-Khanh Le3Jinyi Lee4Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi 12116, VietnamFaculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi 12116, VietnamFaculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi 12116, VietnamFaculty of Marine Engineering, Vietnam Maritime University, Haiphong 180000, VietnamIT-Based Real-Time NDT Center, Chosun University, Gwangju 61452, KoreaAn 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<sup>3</sup> with only 20% of training data and could be increased to 99.0% with 60–80% of the training data.https://www.mdpi.com/2076-3417/12/10/5175electromagnetic testingrivet corrosionaircraft intakehall sensor arraymachine learning |
| spellingShingle | Minhhuy Le Van Su Luong Dang Khoa Nguyen Dang-Khanh Le Jinyi Lee Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach Applied Sciences electromagnetic testing rivet corrosion aircraft intake hall sensor array machine learning |
| title | Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach |
| title_full | Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach |
| title_fullStr | Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach |
| title_full_unstemmed | Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach |
| title_short | Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach |
| title_sort | auto detection of hidden corrosion in an aircraft structure by electromagnetic testing a machine learning approach |
| topic | electromagnetic testing rivet corrosion aircraft intake hall sensor array machine learning |
| url | https://www.mdpi.com/2076-3417/12/10/5175 |
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