Data Science Conference11-12 October 2016Belgrade, Serbia

Aircraft challenge

Marko, don’t forget to show

the aircraft to the audience

Dr David Warren

3400 G shock for 6.5 ms

500 lb. Dropped from 10 ft with a ¼-inch-diameter contact point

1100 ºC flame for 30 minutes.

260 ºC for 10 hours

Immersion in aircraft fluids for 24 hours

Immersion in sea water for 30 days

5,000 pounds crush for 5 minutes on each axis

Pressure equivalent to depth of 20,000 ft.

Avoiding Black Boxes with Data ScienceRaffaele Rainone & Marko Vasiljevski

Data Science Conference11-12 October 2016Belgrade, Serbia

Hkjk;l

Dr Raffaele Rainone

Hkjk;l“I don’t know…, pure mathematician, Python developer, data scientist, pizza lover, feeling a bit home sick for Italy, …”

Chatting about…

Aviation Safety

A story about flight data monitoring (FDM)A story of a flight data analyst

A story of a flight safety statistician

Chatting about…

Data science in (a bit of) action

Jet-engine health - Working with Mr. BayesDetecting safety concerns – PCA, a friend

Finding cuckoo’s eggs – Mr. Markov’s chains

Flight Data Monitoring (FDM)

Flight Data Monitoring (FDM)

Flight Data Monitoring (FDM)

Flight Data Monitoring (FDM) what?!

Part of safety management system (SMS)Airlines worldwide obliged to do FDMSpotting deviations from safe operationNon-punitive – learn from mistakesConfidential

Quick Access Recorder

Flight Data Recorder

Data Acquisition Unit

002AF5C0 00 00 76 07 04 01 64 08 06 01 3C 0C F2 0E 3C 0C ..v...d...<.ò.<.002AF5D0 00 00 58 02 02 0C 00 0C 48 08 00 00 00 00 00 0C ..X.....H.......002AF5E0 00 0C 00 00 00 00 E0 0F 00 00 00 0C 40 0C 00 0C ......à.....@...002AF5F0 00 00 00 00 00 00 00 00 00 00 00 00 00 00 90 01 ...............�002AF600 47 02 77 07 03 00 2B 0E DF 0F 02 0C A5 0F 78 00 G.w...+.ß...¥.x.002AF610 55 04 D3 01 D8 0F EA 03 A4 0F EE 0F FD 0F 00 00 U.Ó.Ø.ê.¤.î.ý...002AF620 08 00 10 00 00 00 08 00 11 00 00 00 06 00 1E 00 ................002AF630 F8 0F 10 00 00 00 8C 0B 0C 00 0A 00 00 00 00 00 ø.....Œ.........002AF640 00 00 77 07 96 0F 64 08 06 01 3C 0C F2 0E 08 08 ..w.–.d...<.ò...002AF650 00 00 00 00 01 00 AC 00 4C 08 01 08 00 00 00 00 ......¬.L.......002AF660 00 00 04 02 D0 02 0D 02 12 02 00 0B 44 01 00 00 ....Ð.......D...002AF670 A4 06 40 01 03 00 00 00 40 02 00 00 00 00 00 00 ¤.@.....@.......002AF680 02 00 77 07 00 00 2B 0E DF 0F 04 0C A5 0F 00 08 ..w...+.ß...¥...002AF690 54 04 D4 01 DA 0F EA 03 A4 0F 73 0E 00 00 01 00 T.Ô.Ú.ê.¤.s.....002AF6A0 08 00 10 00 01 00 08 00 00 00 03 00 06 00 1D 00 ................002AF6B0 FB 0F 11 00 02 00 01 00 00 00 03 00 00 00 00 00 û...............002AF6C0 01 00 76 07 00 00 64 08 06 01 3C 0C F2 0E 3C 0C ..v...d...<.ò.<.002AF6D0 6A 02 21 03 03 00 00 00 48 08 00 00 00 00 00 00 j.!.....H.......002AF6E0 00 00 00 00 00 00 08 00 00 00 00 00 00 00 00 00 ................002AF6F0 00 00 00 00 00 00 00 00 00 00 00 00 00 00 FC 0F ..............ü.002AF700 CC 08 77 07 03 00 2B 0E DF 0F 02 0C A5 0F 79 01 Ì.w...+.ß...¥.y.002AF710 54 04 D4 01 D8 0F EA 03 A7 0F EE 0F FE 0F 01 00 T.Ô.Ø.ê.§.î.þ...002AF720 08 00 00 00 00 00 00 00 11 00 02 00 06 00 00 00 ................002AF730 F8 0F 00 00 00 00 8C 0B 00 00 00 00 00 00 00 00 ø.....Œ.........002AF740 00 00 76 07 94 0F 64 08 06 01 3C 0C F2 0E 08 08 ..v.”.d...<.ò...002AF750 9A 0C 18 00 61 0C 68 01 48 08 F8 0F A4 0C 00 00 š...a.h.H.ø.¤...002AF760 00 00 00 00 00 00 00 00 00 00 00 00 00 00 5B 02 ..............[.002AF770 00 00 03 02 00 00 2B 0F F0 0F 2A 0F 00 00 00 00 ......+.ð.*.....002AF780 00 00 77 07 00 00 2B 0E DF 0F 04 0C A5 0F 2C 00 ..w...+.ß...¥.,.002AF790 54 04 D4 01 D8 0F EB 03 A6 0F 00 0D 00 00 01 00 T.Ô.Ø.ë.¦.......002AF7A0 08 00 17 00 03 00 0F 00 00 00 00 00 06 00 1C 00 ................002AF7B0 F8 0F 10 00 02 00 01 00 00 00 03 00 00 00 00 00 ø...............002AF7C0 00 00 76 07 11 00 64 08 06 01 3C 0C F2 0E 3C 0C ..v...d...<.ò.<.

What now?Data frames

Questions, suggestions, congestions?

The story of a flight data analyst

Questions, suggestions, congestions?

The story of a flight safety statistician

The story of a flight safety statistician

STATISTICS

How do we monitor safety in FDM

012345678

Event Count 3.141592653589793238462643383384

And…

Domain knowledgeExperience

Common sense

Uncertainty - good companion

Normal acceleration - Tdwn Normal acceleration – Lift-off

Statistic ValueTotal count 80,663

Average 1.31(Min, Max) (1.03,

2.40)Range 1.37

Standard deviation 0.10

Statistic ValueTotal count 80,663

Average 1.19(Min, Max) (0.66,

1.63)Range 0.97

Standard deviation 0.05

Wider histogram,

less confidence

in mean value

Narrowerhistogram,

more confidence

in mean value

How apples relate to flight safety

Micromanaging events

0369

Top 5 Events – January 2016

Micromanaging events

High sp

eed t

axiing

High la

teral

g tax

iing

Flap o

versp

eed

Airspe

ed hi

gh 10

000-5

000 f

t

Exces

sive b

reakin

gPu

ll up

Stick

shake

r0369

Top 7 Events – January 2016

Most severe safety event – a riddle

It doesn’t happen in the air

It doesn’t happen at the gate

It happens in the office

Micromanaging events

High sp

eed t

axiing

High la

teral

g tax

iing

Flap o

versp

eed

Airspe

ed hi

gh 10

000-5

000 f

t

Exces

sive b

reakin

gPu

ll up

Stick

shake

r0369

Top 7 Events – January 2016

Not looking at your data!

Trending?

Correlation?

Questions, suggestions, congestions?

Enough, it’s a data science conference!

Improving current state

Jet-engine health with Mr. Bayes

Engine performance monitoring

N1 N2

Fuel

Flow

Exhaust Gas

Temperature

Engine performance monitoring

Fuel

Flow

Exhaust Gas

Temperature

Engine performance monitoring

End of take-off flow

End of take-off flow

End of take-off flow

End of take-off flow

End of take-off flow

End of take-off flow

End of take-off flow

End of take-off – a problem

If not detected – problems with engine healthDifficult to do with classical signal processing

What if a parameter fails (not so rarely)

End of take-off – a solution

Time at vertical navigation mode selected (VNAV)Drop/rise in fuel flow (FF) and gas temperature (EGT)Search for changes around that point in timeGather the knowledgeCalculate probability for EOT for unseen flight

End of take-off – a solution

Time at vertical navigation mode selected (VNAV)Drop/rise in fuel flow (FF) and gas temperature (EGT)Search for changes around that point in timeGather the knowledgeCalculate probability for EOT for unseen flight

End of take-off – a solution

End of take-off – a solution

End of take-off – a solution

End of take-off – a solution

End of take-off – a solution

If crazy about this, search for PyData London 2016 videoM. Vasiljevski & R. Rainone

“Python flying at 40,000 feet”

Questions, suggestions, congestions?

Finding new concerns

Novelty detection with principal component analysis

Standardising

mean = 0standard deviation = 1

Formally…

Previous 700 x 123 matrix is A Multiply transpose of A with A to get covariance matrix, C.

It’s 123 x 123 Do singular value decomposition of C to find eigenvectors

and eigenvalues Eigen vectors coincide with directions of highest variance Keep just first couple of vectors to reduce dimensionality

Questions, suggestions, congestions?

Helping business

Flight data upload monitor based on Markov chains

Flight data upload data data data

Flight data upload data data data

Flight data upload data data data

Flight data upload data data data

Flight data upload data data data

Flight data upload data data data

State1 = M x State0State2 = M x State1 = M x (M x State0) = M2

x State0StateN = MN x State0

Don’t have to know states, just powers of M (probabilities)

Flight data upload data data data

Flight data upload data data data

Flight data upload data data data

Flight data upload data data data

Flight data upload data data data

Flight data upload data data data

Questions, suggestions, congestions?

Credits

CHRIS JESSE

RAFFAELE (PIZZA) RAINONE

MARTA

VASI

LJEV

SKI

MILA

N

BOROTA

MILIC

A IVA

NIŠEV

IĆ+ foeniculumvulgare

ZIZI(paid for my tickets)

Main takeaways from this talk

LEARN

HAVE FUN

SHARE KNOWLEDGE

THINK

PLAY

THANK YOU & SAFE FLYING