Advancing Aircraft Maintenance Through Artificial Intelligence: A Comprehensive Review and Future Perspective

Objectives

This paper identifies trends in AI for aviation maintenance. Aiming to uncover untapped

benefits, find research gaps, and provide insight into the current state of research and active

applications.

Methods

The method of research is a manual review of research papers, following the structured PRISMA

method. Research papers are collected and filtered, before key data points are extracted through

reading and keyword search. Papers selected were required to be related specifically to aviation

maintenance, written in English, and published in the current landscape of AI, with increasing

adoption of generative AI (year >= 2018).

Findings

Findings reveal that AI research geared towards aviation is an emerging topic, with a general

year-over-year increase in the number of research papers published. The research reveals several

findings: Nuanced topics such as ethical considerations in the use of AI in aviation are yet to be

understood completely, and require further exploration, with relatively few papers (n = 29)

focusing on explainability and even fewer (n = 3) focusing primarily on the development of xAI;

The study of practical applications of AI in maintenance, and research into complex systems

that utilise AI-driven subsystems, such as Computer Vision, Digital Twins and Robotic

Inspection, have already started industry use and show promise for further enhancements in the

future. These systems trend high for research in recent years (n = 14, n = 13, and n = 10,

respectively); Few papers research the creation of datasets (n = 2), and a significant number (n

= 53) use the C-MAPSS synthetic dataset, suggesting that a gap exists in research and

development for datasets specific to aviation maintenance.

Application/Improvements

The paper provides insight on research and trends surrounding aviation maintenance, possible

gaps, and possible avenues for further research. There is a risk of bias due to research method

limitations.