RTX Units Use AI and Data Analytics to Modernize Aircraft Maintenance and Cut Unscheduled Repairs
Why It MattersThe shift toward AI-driven predictive maintenance signals a broader MRO industry move from reactive repairs to prescriptive, data-based fleet lifecycle management.
RTX said maintenance teams across its facilities are using artificial intelligence and data analytics to speed inspections, predict component failures earlier and reduce unscheduled repairs. At Pratt & Whitney, borescope inspections of turbine blades and compressors traditionally required many hours of image review and manual report writing; the company acquired Aiir Innovations in mid-2026, whose AI borescope tool has been used on the V2500 engine and piloted on the GTF and F135 engines, with plans for wider rollout. The software counts blades, tracks surface patterns and flags areas for closer review, with the operator still making final decisions.
At a Singapore facility, a machine-vision system called "Spot" scans quick-engine-change kits of more than 150 items to check completeness before installation, cutting manual workload by roughly 70%, while laser-guided ply-installation systems improve composite repair accuracy and reduce rework.
Collins Aerospace's Ascentia platform processes thousands of flight-data parameters per second alongside maintenance records to flag repair needs before cockpit alerts or visible wear appear; operators using it have reduced unscheduled component removals and minimum-equipment-list write-ups. Its FlightSense service turns those alerts into advance parts planning, pre-positioning inventory and adjusting MRO output, and in one long-haul fleet it allowed Collins to prioritise aircraft and reposition inventory before cooling-unit wear caused any schedule disruption. RTX said its next step is linking Pratt & Whitney's inspection AI with Collins Aerospace's predictive analytics into a unified MRO ecosystem spanning design, testing, manufacturing and service-life data.
