Big Data in Airbus Flight Test and Integration Center OOW2015 - AIRBUS 26th of october 2015 ORACLE open world 2015 Laurent PELTIERS Jean-Marc WATTECANT Big Data in Airbus Flight Test and Integration Center

Some figures about AIRBUS OOW2015 - AIRBUS 26th of october 2015 Some figures about AIRBUS 74000 employees over mainly 4 countries (more than 1000 in the US) Revenues (2013) € 42,000 million / EBIT € 1,710 million 8121 aircrafts in service operated by more than 400 companies Largest civil aircraft: the double deck A380 629 aircrafts delivered in 2014 2’ Ce qui est important pour les EV: + en + transnational 3 FAL Nombre d’A/C livrés

Flight & Integration test center : our place in A/C development OOW2015 - AIRBUS 26th of october 2015 Flight & Integration test center : our place in A/C development A/C certified! MG3 MG13 MG7 MG9 MG11 MG4.1 Entry into concept Entry into service MG5 MG6 Auth. to Offer Entry into Definition End of Concept Entry into Production Freeze of Definition Entry into FAL First Flight Data for Manufacturing Integration Tests Flight Tests Production Assembly line Ramp Up Concept Definition 6 Year Development lead time ~24 months MG4.2 Test Phase 3’ Incrémental développement + time to market => cycles de plus en plus rapides 2 parties historiquement dissociées sol / vol => intégration de plus en plus forte 3

Flight & Integration test center: our mission OOW2015 - AIRBUS 26th of october 2015 Flight & Integration test center: our mission Design office & program domains Tests definition Aircraft certification Flight &Integration test center Tests preparation Tests reports Ground or flight tests Tests analysis 2’ Rôle des EV Certification Maturité à l’EIS Amélioration continue des modèles

Evolution of data collected OOW2015 - AIRBUS 26th of october 2015 Evolution of data collected Parameters # x50 670 000 parameters 450 TB archived 320 000 parameters 150 TB archived 14 000 parameters Data archived 12,8 TB archived x50 12 000 parameters 8,5 TB archived AMPEX 28 tracks 8 GB SONY AIT 2/3 50/100 GB SCSI hard disk 300 GB SSD hard disk 700 GB Flight and integration center: the context From the 80’s Increase of the parameters collected Sample rate of analog sensors is up to 48khz and 400khz for specific usage AFDX buses from the A380 Switch from PCM to IENA At the same time the recorders changed from the 28 tracks tapes to the last generation of RAID SDD hard disk But the way of working is still the same 1987 1992 2005 2013

Why Big data ? - Volume Forecast OOW2015 - AIRBUS 26th of october 2015 Why Big data ? - Volume Forecast A lot of restore up to 10 years after Concurrent access High volume management Before the A350 campaign we estimate the forecast of the data collected in the 4 next years The beginning of the NEO campaign was before the ramp-down of the A350 with up to 7 aircrafts involved . There was a risk to slow down the analysis An other point is about the restore of old data to compare the legacy aicrafts and the new version

Big data project: One year project OOW2015 - AIRBUS 26th of october 2015 Big data project: One year project RFI/RFP: Q3 2013 Start:feb 2014 Validation: october 2014 EIS: january 2015 Planning ambitieux pour une entreprise de cette taille avec une initiative bottom-up

Big data project: the scope of the first step OOW2015 - AIRBUS 26th of october 2015 Big data project: the scope of the first step Acquire Raw data Organize Current tools compatibility Data triggering Analyse Machine learning… Decide Discovery tools No functional impact for the user Enhancements : Less overhead to get the data Full campaign on-line

Large time series Injection by burst Main challenges ? OOW2015 - AIRBUS 26th of october 2015 Main challenges ? Big data appliance noSQL database HADOOP cluster Large time series Up to 4 flights 2 times a day Injection by burst

4 aircrafts 70000 parameters / aircrafts 700h of flights OOW2015 - AIRBUS 26th of october 2015 Current status 4 aircrafts 70000 parameters / aircrafts 700h of flights Big Data Appliance X4-2 6 nodes noSQL 6 nodes HADOOP 5 billions of events 15% of the disk capacity 50% of the NEO processing use the appliance Time saving for multiflights analysis

Next steps: processing OOW2015 - AIRBUS 26th of october 2015 Next steps: processing Descriptive analysis: Complex event detection Wide correlation between parameters Predictive analysis: Sensor failure Anomalies database

Next steps: Vizualisation OOW2015 - AIRBUS 26th of october 2015 Next steps: Vizualisation

AIRBUS Flight and Integration Test Centre : our data processing view OOW2015 - AIRBUS 26th of october 2015 AIRBUS Flight and Integration Test Centre : our data processing view Processing HMI Data mining tool Complex event detection Time correlation for diag Machine learning algo Full campaign on-line Results storage Flight Tests data lake

Big Data application : Improve « Data retrieval » OOW2015 - AIRBUS 26th of october 2015 Big Data application : Improve « Data retrieval » Save flight hours Easy search will be generalized « search  autopilot on & altitude > 30000 ft & Mn>0.8 & bank > 5° then and plot the vertical load factor » Opportunities to bridge with other database (configuration, logs,…) Compare versions in the frame of incremental development If can be specific flight , ferry flight : not all of this have been gathered in DB It may prevent from reflying if conditions are met (5% tolernace on weight increase for example) A320 :1988 Sharklet 2012 NEO 2015

Big Data application : Perform « Data re-use » OOW2015 - AIRBUS 26th of october 2015 Big Data application : Perform « Data re-use » Increase system design maturity use the data for design verification prior implementation in avionic software (robustness) Continuous detection of anomalies on the whole campaign Tuning and re-launch surveillance algorithms Patterns recognition out of the « usual » enveloppe

Big Data application : Accelerate « Test Analysis » OOW2015 - AIRBUS 26th of october 2015 Big Data application : Accelerate « Test Analysis » Enhance the analysis through statistical tools Correlation analysis: eg : longitudinal oscillation A380 specific flight needed to understand the root cause Mn=0.85->0.8 Mn=0.85 No stimulation stimulation « Self » learning and clustering capability Attention correlation ‘n’est pas causalité mais cela peut aider à identifer un phénomène

OOW2015 - AIRBUS 26th of october 2015 © AIRBUS Operations S.A.S. All rights reserved. Confidential and proprietary document. This document and all information contained herein is the sole property of AIRBUS Operations S.A.S. No intellectual property rights are granted by the delivery of this document or the disclosure of its content. This document shall not be reproduced or disclosed to a third party without the express written consent of AIRBUS Operations S.A.S. This document and its content shall not be used for any purpose other than that for which it is supplied. The statements made herein do not constitute an offer. They are based on the mentioned assumptions and are expressed in good faith. Where the supporting grounds for these statements are not shown, AIRBUS Operations S.A.S will be pleased to explain the basis thereof. AIRBUS, its logo, A300, A310, A318, A319, A320, A321, A330, A340, A350, A380, A400M are registered trademarks.