AI-Powered Predictive Maintenance in Aviation Operations

This article explores the transformative impact of AI-powered predictive maintenance on base and line maintenance operations in the aviation industry. The study addresses the limitations of traditional maintenance practices by integrating advanced technologies, including machine learning, IoT sensors, and big data analytics, to enhance operational safety, reliability, and cost-efficiency. A mixed-methods research design was adopted, combining quantitative data from maintenance logs, sensor outputs, and cost reports with qualitative insights obtained through semi-structured interviews with industry experts. Analysis of key performance indicators (KPIs) such as Mean Time Between Failures (MTBF), Fault Detection Rate (FDR), and Maintenance Cost per Available Seat Kilometer (CASK) revealed significant improvements in technical performance and operational efficiency. The findings indicate that AI-driven predictive maintenance can reduce maintenance costs by 12-18% and decrease unplanned downtime by 15-20%, thereby increasing aircraft availability. However, challenges related to data quality, integration with legacy systems, regulatory compliance, and high initial investments persist. The study concludes that strategic partnerships, phased implementation, and targeted workforce training are essential for the successful adoption of AI technologies in aviation maintenance. This research contributes to the growing body of knowledge on digital transformation in aviation, providing a roadmap for enhancing maintenance practices and ensuring sustainable operational performance.