THE PROBLEM

An international airline operating in Western Pacific region and deploying a fleet of 250 aircrafts wanted to reduce costs by now flight engine failure in advance to convert unscheduled maintenance to planned maintenance

Analysis of condition of in-service equipment allows the airline to schedule maintenance proactively.

The client wanted to minimize component failure, reduce unscheduled part-removals and avoid aircraft-on-ground.

THE SOLUTION

A proprietary self-learning, multi-modeling, predictive AI technology from Findability.AI was used to analyze the sensor data from aircraft.

External variables like meteorological conditions and structural specifications for materials used in aircraft components were factored in to create accurate prediction model.

The propensity of failure for 53 critical components was predicted with almost 97% accuracy

THE SUCCESS

Adoption of predictive maintenance reduced the overall aircraft downtime by about 35% also minimizing the cost of spare parts and supplies.

Irreversible component failures reduced by 25% during the test period resulting in millions of dollars worth of savings for the client.