| Full text | |
| Author(s): |
Brandoli, Bruno
[1]
;
de Geus, Andre R.
[2]
;
Souza, Jefferson R.
[2]
;
Spadon, Gabriel
[3]
;
Soares, Amilcar
[4]
;
Rodrigues, Jr., Jose F.
[3]
;
Komorowski, Jerzy
[5]
;
Matwin, Stan
[1, 6]
Total Authors: 8
|
| Affiliation: | [1] Dalhousie Univ, Inst Big Data Analyt, Dept Comp Sci, Halifax, NS B3H 1W5 - Canada
[2] Univ Fed Uberlandia, Dept Comp Sci, BR-38400902 Uberlandia, MG - Brazil
[3] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos - Brazil
[4] Mem Univ Newfoundland, Dept Comp Sci, St John, NF A1C 5S7 - Canada
[5] JPWK Aerosp, Ottawa, ON K1A 0R6 - Canada
[6] Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw - Poland
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | SENSORS; v. 21, n. 12 JUN 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols. (AU) | |
| FAPESP's process: | 17/08376-0 - Analysis and improvement of urban systems using digital maps in the form of complex networks |
| Grantee: | Gabriel Spadon de Souza |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 18/17620-5 - Preventive medicine by means of deep learning techniques applied in healthcare prognosis |
| Grantee: | José Fernando Rodrigues Júnior |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 19/04461-9 - Advancing medical prognosis based on graph concepts and artificial neural networks |
| Grantee: | Gabriel Spadon de Souza |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 14/25337-0 - Design of vertex-centric algorithms for pattern recognition on large-scale graphs using asynchronous parallel processing |
| Grantee: | Gabriel Perri Gimenes |
| Support Opportunities: | Scholarships in Brazil - Doctorate |