A Systematic Review of The Impacts of Predictive Maintenance Using AI in the Aerospace Industry

Airlines and aerospace companies continuously seek to reduce operational costs while maintaining

the highest safety standards. Since aircraft maintenance constitutes a significant portion of these costs,

manufacturers and operators are exploring new methods to improve efficiency. One promising approach

is predictive maintenance, which leverages sensor data and machine learning algorithms to predict

component or system failures before they occur. Maintenance can then be scheduled at optimal times

to maximize component lifespan and minimize downtime. This paper employs a systematic literature

review method analyzing 60 sources to evaluate the impacts of predictive maintenance in the aerospace

industry. The findings indicate that predictive maintenance offers several benefits, including reduced

operational costs, enhanced safety, lower environmental impact, and improved passenger experience.

However, implementation challenges remain, particularly regarding data availability, lack of regulatory

standardization, limited technical expertise, and integration into legacy aircraft systems. This topic was

selected due to its significant potential as an emerging technology with transformative implications for

aviation. The primary limitation of this study is the limited number of available case studies; however,

all relevant cases identified were included in the analysis.