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Deep learning approaches to aircraft maintenance, repair and overhaul: a review

Rengasami, Divish; Morvan, Herve; Patrocinio Figueredo, Grazziela

Authors

Divish Rengasami

Herve Morvan



Abstract

The use of sensor technology constantly gathering aircrafts' status data has promoted the rapid development of data-driven solutions in aerospace engineering. These methods assist, for instance, with determining appropriate actions for aircraft maintenance, repair and overhaul (MRO). Challenges however are found when dealing with such large amounts of data. Identifying patterns, anomalies and faults disambiguation, with acceptable levels of accuracy and reliability are examples of complex problems in this area. Experiments using deep learning techniques, however, have demonstrated its usefulness in assisting on the analysis aircraft health data. The purpose of this paper therefore is to conduct a survey on deep learning architectures and their application in aircraft MRO. Although deep learning in general is not yet largely exploited for aircraft health, from our search, we identified four main architectures employed to MRO, namely, Deep Autoencoders, Long Short-Term Memory, Convolutional Neural Networks and Deep Belief Networks. For each architecture, we review their main concepts, the types of problems to which these architectures are employed to, the type of data used and their outcomes. We also discuss how research in this area can be advanced by identifying current research gaps and outlining future research opportunities.

Citation

Rengasami, D., Morvan, H., & Patrocinio Figueredo, G. (2018). Deep learning approaches to aircraft maintenance, repair and overhaul: a review. In 21st IEEE International Conference on Intelligent Transportation Systemshttps://doi.org/10.1109/ITSC.2018.8569502

Conference Name IEEE International Conference on Intelligent Transportation Systems
Start Date Nov 4, 2018
End Date Nov 7, 2018
Acceptance Date Jun 25, 2018
Online Publication Date Dec 10, 2018
Publication Date Nov 7, 2018
Deposit Date Oct 8, 2018
Publicly Available Date Mar 29, 2024
Print ISSN 1524-9050
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Book Title 21st IEEE International Conference on Intelligent Transportation Systems
Chapter Number n/a
ISBN n/a
DOI https://doi.org/10.1109/ITSC.2018.8569502
Public URL https://nottingham-repository.worktribe.com/output/1049417
Related Public URLs https://www.ieee-itsc2018.org/