Using AI to Transform Aircraft Maintenance and Operations - A Story of Days to Engine Failure

At a modern airline operations control centre, predictive maintenance systems now aspire to flag early warnings based on real-time engine health data.

- SANJAY DESHMUKH COO, Findability Sciences

When a forecast indicates that an engine is likely to exceed performance limits within a defined number of days, maintenance planners can proactively adjust flight schedules, arrange for spare parts, or schedule shop visits in advance. What once triggered emergency responses or unscheduled groundings is now managed through data-driven foresight. This transformation reflects how predictive Al modelling has turned potential disruptions into planned maintenance activities, improving operational reliability and reducing cost without compromising safety.

BEFORE AI: MAINTENANCE BY PATTERN, GUT AND SCHEDULE

Fifteen years ago, an engine showing subtle changes in exhaust gas temperature margins or vibration signatures would generate a paper trail: a technician's note on a line maintenance card, a call into TechOps, and a conservative defensive response — ground the aircraft if in doubt, or ferry it to a maintenance base the next time it could be scheduled.

Preventive maintenance relied heavily on flight-hour or cycle-based schedules, inspections driven by OEM service bulletins, and human judgment informed by experience. This approach protected safety but was inefficient: unnecessary shop visits, emergency AOG events, and expensive part shipments were common.

The limitations were structural: data lived in silos, sensor streams were high-volume but noisy, and no operational system translated complex telemetry into a simple operational decision such as “this engine needs a shop visit within X days.” That translation — days to failure — is what airlines now prize, because calendar days map directly to planning windows for crews, aircraft rotations and spare logistics.

THE SHIFT: COMBINING DATA, TWINS AND AI