Airlines

Vueling pioneers predictive maintenance using artificial intelligence to optimise operations

Vueling, part of the IAG Group, has integrated Airbus’ Skywise Predictive Maintenance technology into its fleet maintenance digitalisation process.

The aim is to anticipate and plan unscheduled maintenance of its fleet further in advance, thus achieving greater optimisation of its operations.

In recent years, Vueling has successfully built an artificial intelligence-based ecosystem to address key challenges in operational maintenance efficiency.

As part of this strategy, the project began with the mass digitisation of aircraft and engine data.

This process improves maintenance planning, optimises the execution of scheduled tasks, and synchronises fleet plans, capacity and availability for the execution of maintenance tasks.

All this with real-time monitoring of maintenance in progress, resulting in much more accurate management.

Now, the company is taking a further step in this digital transformation process, as part of its transformation plan, by integrating Airbus’ “Skywise Predictive Maintenance” technology, which enables the monitoring of aircraft conditions using the airline’s internal data models to generate predictive alerts for aircraft maintenance.

As a result, the airline is better able to optimise its operations by using machine learning to anticipate unscheduled maintenance events or other unforeseen requirements.

The Airbus solution is the first project driven by the Airbus Digital Alliance, which includes Delta TechOps, General Electric and Airbus, to combine different predictive maintenance algorithms.

This technology covers a wide range of aircraft and engine components from different manufacturers and represents a proven predictive maintenance system with more than 200 algorithms covering different aircraft systems, which Vueling has already installed on 53 aircraft.

Isabel Garcia Alvarez, Director of Operations Planning at Vueling, said, “We are a digitally native company with a strong commitment to innovation.

“With this project, we are investing in the improvement of our operations through predictive models that allow us to anticipate the needs of our fleet and thus offer a better service to our passengers”.

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