Paris, Amstelveen, 14 October 2016 - As part of its MRO Lab innovation program, AFI KLM E&M is implementing PROGNOS, a range of solutions based on exploiting the data from aircraft systems with a view to improving maintenance models and processes. AFI KLM E&M capitalizes on the vast amount of data generated by AIR FRANCE and KLM fleets to develop its PROGNOS solutions, and verify their operational relevance and performance before sharing such innovations with its customers.
One example of the application is PROGNOS Engine Health Monitoring (EHM). This software is being designed to carry out statistical analyses of engine data to enable dynamic monitoring and predict failures using an early warning system, for the fleets of AIR FRANCE and KLM as well as client airlines. PROGNOS EHM is part of a series of projects and initiatives focused on Big Data that have already led to operational solutions such as PROGNOS A380, the early warning and failure monitoring software used for the A380's systems, which extends the solution to bigger data volumes.
Solutions based on the same approach are currently under development to support a range of other critical aircraft systems and components, including on the 787.
A key advantage of AFI KLM E&M's approach to Big Data and predictive maintenance is that it stems from the real operational needs and challenges of AIR FRANCE KLM group airlines.
James Kornberg, AFI KLM E&M Director Innovation, explains: "Having full access to aircraft-generated data, and robust technical expertise of aircraft systems and sub-systems due to its MRO/airline profile, endows AFI KLM E&M with genuine legitimacy to develop this type of solutions. Working closely with data scientists and data engineers, we can test and validate the predictive maintenance solutions we are developing."
FiAs experienced in the field by AFI KLM E&M while using PROGNOS to support AIR FRANCE KLM's engines, components and aircraft, fleet operators can reap significant benefits from these new predictive maintenance technologies, which contribute to avoiding operational disruptions, resulting in increased passenger satisfaction as well as reduced maintenance costs.
