Ensuring their aircraft fleets’ continued availability and reliability is a daily challenge for airlines.
As part of its investment in a long-term project for a major European aircraft manufacturer, an Expleo team is now developing operational reliability analysis and predictive maintenance solutions based on an expert combination of aeronautical systems engineering and refined data analysis.
What is the objective? To continuously improve the operational reliability of the aircraft concerned.
The Challenge
An airliner is a highly connected flying computer, subject to economic and environmental constraints. But also, as a by-product of the increasing sophistication of onboard systems, it is a significant challenge to collect and process data, given that a typical commercial aircraft has an average of several thousand systems. Therefore, ensuring that they are used correctly is a difficult task for the scientists in charge of making aircraft on-board systems ever more efficient, reliable, and intelligent in the face of inevitable technical wear and tear or extreme conditions at altitude.
Given these challenges, the peace of mind card is often won through operational reliability analysis and predictive maintenance. For aircraft manufacturers or airlines, this is a critical asset that will enable them to eliminate all those untimely technical breakdowns that can ground their aircraft. To strengthen its work processes in this area throughout the aircraft’s life cycle – from the production phase to the end of its operating period – a major European aircraft manufacturer has decided to call upon Expleo to conduct technical investigations into the performance of the components fitted to its various aircraft models.
Solutions
The expert and versatile Expleo team deployed on the ground today consists of up to fifteen people covering all the required fields, from aeronautical system engineering to data collection and analysis. Whether they are creators of complex mathematical models capable of translating all the parameters to be considered or systems engineers with solid experience in aeronautics (some of whom are keen private pilots), the Expleo team on this project meets the needs for technical monitoring of everything to do with the use and wear of the engines, hydraulic systems, navigation systems or power supply.
With Expleo in charge of the project, the European aircraft manufacturer benefits from all the innovative spirit and analysis capacity needed to meet the complex challenges of maintaining and ensuring the reliability of an airliner throughout its life cycle. Using technologies such as the Python programming language and the Apache Spark massive data analysis engine, Expleo’s team has already achieved notable initial results, providing its customer with additional stepping stones to become a technology company that also offers value-added digital services for airlines.
Outcome
The Expleo team has succeeded in establishing a Big Data analysis model capable of better determining and listing areas of GPS interference. In a few months, no less than 12 TB of GPS data were sifted through to anticipate any potential transmission problems effectively. The same success was achieved in the predictive analysis of filter clogging.
Despite the data provided by the available sensors, anticipation was not always evident in practice. Thanks to the Big Data analyses developed by Expleo, its customer can now better anticipate filter replacement.
In terms of meeting aircraft delivery dates, resolving ground or in-flight problems more quickly, or simply getting the right information at the right time, operational reliability analysis and predictive maintenance have many advantages for our customers. With its proven expertise in data science and aeronautical systems engineering, Expleo is delivering significant results and again proving that it has what it takes to meet this customer’s ambitions.
DIGITAL TRANSFORMATION
Big data, analytics, AI & advanced algorithms
Expleo builds modern data analytics strategies by deploying machine learning and advanced algorithms. This empowers us to create complex predictive and governing models, both in the industry and services environments.
