Con2188 Good Con2188 Peltiers Oow2015 Airbus Bigdata

Big Data in Airbus Flight Test and Integration Center

26th of october 2015 OOW2015 - AIRBUS

ORACLE open world 2015 Laurent PELTIERS

Jean-Marc WATTECANT

© AIRBUS Operations S.A.S. All rights reserved. Confidential and proprietary document.

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




MG3 MG13 MG7 MG9 MG11 MG4.1 MG5 MG6

Data for Manufacturing

Integration Tests Flight Tests

Production Assembly line Ramp Up

Concept

Definition

6 Year Development lead time

~24 months

~24 months

MG4.2

Flight & Integration test center : our place in A/C development

Test

Phase

A/C certified!


Flight &Integration test center

Design office & program domains

Flight & Integration test center: our mission


Tests definition

Tests preparation

Ground or flight tests Tests analysis

Tests reports

Aircraft certification


Evolution of data collected


150 TB archived 320 000 parameters

12 000 parameters

12,8 TB archived

Parameters #

14 000 parameters

670 000 parameters

Data archived

x50

x50

8,5 TB archived

450 TB archived

AMPEX 28 tracks

8 GB

SONY AIT 2/3

50/100 GB

SCSI hard disk

300 GB

SSD hard disk

700 GB



Big data project: One year project



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


Main challenges ?


Big data appliance

• noSQL database

• HADOOP cluster

Up to 4 flights 2 times a day

http://en.wikipedia.org/wiki/File:Random-data-plus-trend-r2.png

https://www.google.fr/url?url=https://cloud.google.com/hadoop/&rct=j&frm=1&q=&esrc=s&sa=U&ei=Re1dVePJK4neU-GzgbAH&ved=0CDUQ9QEwBA&usg=AFQjCNGGLOUW39-UHlKdZHulClsmyl9KPA


Current status

4 aircrafts

70000 parameters / aircrafts

700h of flights


5 billions of events

15% of the disk capacity

50% of the NEO processing use the appliance

Time saving for multiflights analysis

Big Data Appliance X4-2

6 nodes noSQL

6 nodes HADOOP


Next steps: processing


Predictive analysis:

• Sensor failure

• Anomalies database

Descriptive analysis:

• Complex event detection

• Wide correlation between parameters

https://spark.apache.org/

http://www.r-project.org/index.html


Next steps: Vizualisation




AIRBUS Flight and Integration Test Centre : our data processing view

Full campaign

on-line

Complex event detection

Time correlation for diag

Machine learning algo

Results storage

Flight Tests data

lake

Processing HMI Data mining tool

http://www.google.fr/url?url=http://www.itclips.net/2013/03/25/when-nosql-databases-are-yes-good-for-you-and-your-company/&rct=j&frm=1&q=&esrc=s&sa=U&ved=0CDQQwW4wDzgUahUKEwiBsLeS66_HAhUlpnIKHVdfDuM&usg=AFQjCNGUmtEUoRmWPG1HxZIOjGnVVToxjw

http://rprogramming.net/wp-content/uploads/2012/10/RStudio-Screenshot.png


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


A320 :1988 Sharklet 2012 NEO 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 »

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 Mn=0.85

No stimulation stimulation No stimulation

• « Self » learning and clustering capability



© 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.

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