Predictive maintenance using AI in aircraft leverages machine learning, IoT sensors, and real-time analytics to monitor engine health, structural integrity, and critical components, enabling early fault detection before failures occur. By analyzing massive datasets from flight operations, AI models predict wear, optimize maintenance schedules, and reduce unscheduled downtime. Applications include commercial airlines, defense aviation, cargo fleets, and private aircraft operations. Key benefits are enhanced safety, reduced maintenance costs, extended asset life, and improved fleet availability. Innovations such as digital twins, cloud-based predictive platforms, and blockchain-enabled maintenance logs are reshaping aerospace operations. Market growth is fueled by rising air traffic, stringent aviation safety regulations, and the push for cost efficiency in the global aviation sector.
