ATM sa safety management: rea eactive an and pr proactive ind - - PowerPoint PPT Presentation

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ATM sa safety management: rea eactive an and pr proactive ind - - PowerPoint PPT Presentation

Dec 07, 2023 126 likes •304 views

Giulio Di Gravio 1 , Maurizio Mancini 2 , iarca 1 , Francesco Costantino 1 Riccardo Patr tria 1 Department of Mechanical and Aerospace Engineering 2 ENAV s.p.a. Operations Directorate Fourth Fou h SESAR R Inno nnovatio tion Days th November

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SLIDE 1

Fou Fourth h SESAR R Inno nnovatio tion Days

25 25-27 27th

th November

r 2014 2014

ATM sa safety management: rea eactive an and pr proactive ind indicators

Forecasting and monitoring ATM overall safety performance Giulio Di Gravio1, Maurizio Mancini2, Riccardo Patr tria iarca1, Francesco Costantino1

1Department of Mechanical and Aerospace Engineering 2ENAV s.p.a. Operations Directorate

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 2

WHE HERE DO DO WE E STARTED (i (i kno know you you kno know it it)

EUROCONTROL is developing a ha harm rmonised framework rk for

  • r

the he sa safety re regu gula latio ion of

  • f ATM, for implementation by States.

Its core is represented by ESARRs

ESARRs

Safety Oversight in ATM Reporting and Assessment of Safety Occurrences in ATM. Use of Safety Management System by ATM Service Providers. Risk Assessment and Mitigation in ATM. ATM Services’ Personnel Software in ATM Systems

In Italy ENAV s.p.a. follows ESARRs. We analysed the safety events’ reported data collected from 2008.

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SLIDE 3

Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 3

REA REASON SWISS CHEE EESE MOD MODEL ICEB EBERG OF OF SA SAFETY THESE MODELS are

  • Clear
  • Evocative
  • Easy to
  • un

understand BUT … HOW TO O APPLY THEM FOR EVALUATING THE GLOBAL SA SAFETY LEVELS OF ATM?

WHE HERE DO DO WE E STARTED (al also thes hese thi hings…)

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 4

REPORTING DATABASE NUMBER OF OCCURRENCES STANDARD INDICATORS

APF INDICATORS

ESARRs’ reporting structure + ENAV s.p.a. reporting database permit the construction of SMARTER and HOLISTIC INDICATORS

ATM related safety occurrences

Accidents

Mid Air Collision CFIT Collision

Incidents

Near Collision Potential for collision or near collision

ATM - specific

  • ccurrences

Inability to provide ATMS Failure of ATM service

WHE HERE WE E WOUL ULD LI LIKE TO GO GO (no now you you kno know al also it! it!)

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SLIDE 5

Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 5

RE REACT CTIV IVE INDIC ICATORS (stepwise saf afety 1/ 1/2)

  • Determine the organizational factors that influence performance (ESA

SARR 4)

  • Determine the information available on those factors (Litera

rature re analy nalysis is)

  • Organize the influencing factors (Accid

cidents/ s/In Inci cidents/Is Issues)

  • Determine the relative importance of the factors (AHP

AHP)

  • Display information for decision makers (Sa

Safe fety Ind ndex)

AER EROSPACE PE PERFORMANCE FACTOR

  • Structure the problem (Min

Mind Ma Map)

  • Construct a set of pairwise comparison matrices (Subject matter experts’ evaluations)
  • Use the comparison to obtain the weights (FAA: Rob

Robust Mod Model)

ANALYTIC IC HIE HIERACHY PR PROCESS

𝐹𝑤𝑓𝑜𝑢𝑗 𝐵𝑄𝐺 𝑇𝑏𝑔𝑓𝑢𝑧 𝐽𝑜𝑒𝑓𝑦 = 𝐹𝑤𝑓𝑜𝑢𝑗𝑏𝑜𝑜𝑣𝑏𝑚 𝑑𝑝𝑣𝑜𝑢 𝑈𝑃𝑈𝐵𝑀 𝑢𝑠𝑏𝑔𝑔𝑗𝑑 𝑑𝑝𝑣𝑜𝑢 𝐹𝑤𝑓𝑜𝑢𝑗 𝐵𝐼𝑄 𝑥𝑓𝑗𝑕ℎ𝑢 𝑩𝑸𝑮 𝑻𝒃𝒈𝒇𝒖𝒛 𝑱𝒐𝒆𝒇𝒚 𝒌 =

𝒋 𝒐

𝑭𝒘𝒇𝒐𝒖𝒋 𝑩𝑸𝑮 𝑻𝒃𝒈𝒇𝒖𝒛 𝑱𝒐𝒆𝒇𝒚

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 6

EN EN-ROUTE

SAFETY INDEX 1 ENR SAFETY INDEX 2 (ATM) ENR

We have buil ilt 4 Indexes:

Accidents (0,5)

Accidents (1,000)

Events (0,35)

Near collision (0,752)

  • INSA + INSN
  • NCFIT
  • RINA + RINN (AAY)
  • SMI
  • TWI (AAY)

Potential for collision or near collision (0,107)

  • DATC / DATS
  • LBS
  • PSMI
  • REX
  • RINA+RINN (AAN)
  • TWI (AAN)
  • UPA

System failure (0,142)

  • AIS
  • ASP
  • MET

Issues (0,15)

Procedural (0,375)

  • ATO
  • MA
  • EME
  • PRI

External (0,176)

  • BS
  • LASER
  • TRA
  • WS
  • OTH

Communication (0,448)

  • SCS
  • CSC
  • PLCC

SAFETY INDEX 1 APT

AIRP RPORT

SAFETY INDEX 1 APT SAFETY INDEX 2 (ATM) APT

RE REACT CTIV IVE INDIC ICATORS (stepwise saf afety 2/ 2/2)

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SLIDE 7

Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 7

We obtained RE REACT CTIV IVE safety ana nalysis based on:

  • ENAV s.

s.p.a. p.a. sa safe fety da database

  • APF Sa

Safe fety Ind ndexes

We aim to obtain PR PRO-ACT CTIV IVE safety analysis RE REACT CTIV IVE vs PR PRO-ACT CTIVE INDIC ICATORS (no (no cla clash, , bo both ar are winn nner ers)

2014 2011 2012 2013

?

Safety Index

REACTIVE SAFETY PRO-ACTIVE SAFETY

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SLIDE 8

Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 8

PR PRO-ACT CTIVE SAFE FETY: 3 3 strategies (al all 3 3 roa

  • ads lead

ead to

  • Rome saf

afety)

SAFETY MONITORING

MONTECARLO

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 9

  • Try to fit various probability distribution to data
  • Select, for each event type the BEST

BEST distribution

The BEST T one ne according to the AIC rank that considers the prin inciple of par arsim imony and has a good accu curac acy/ea ease e rati tio in case of many input data

\ TRA gen-08 12 feb-08 8 mar-08 5 apr-08 4 mag-08 10 giu-08 11 lug-08 1 ago-08 17 set-08 13

  • tt-08

3 nov-08 5 dic-08 7 gen-09 5 feb-09 2 mar-09 12 apr-09 8 mag-09 20 giu-09 23 lug-09 3 ago-09 9 set-09 24

  • tt-09

7 nov-09 5 dic-09 9 gen-10 7 feb-10 5 mar-10 6 apr-10 6 mag-10 13 giu-10 17 lug-10 12 ago-10 8 set-10 11

  • tt-10

10 nov-10 15 dic-10 10

DIST STRIBUTION FI FIT FOR EA EACH EVEN ENT TYPE APF F PROCESS

Probability Relative Frequency TRA Number of Occurrences

HI HISTORIC IC FI FIT T (as as thi hings di did, , thi hings will do do)

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 10

TI TIME SER SERIE IES ANALYSIS S (fore

  • recast with

h mat maths)

  • Based on Yule’s and Box and Jenkins’ theories a linea

ear fi filter has been built for each event type, according to its characteristics (AR, MA, ARMA, ARIMA, etc.)

  • Analyzing (transformation, trends, seasonality):

mean, μ volatility parameter, σ

EVENT TYPE HISTORIC SERIES

TIME E SERIES ANAL NALYSI SIS S FOR EA EACH EVEN ENT TYPE APF F PROCESS

TRA Safety Index 1 ENR Contribution

Time [months]

HISTORIC FIT (as things did, things will do)

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 11

CA CAUS USAL L FI FIT T (roots ar are e fr frui uits 1/ 1/2)

Noise Weight Human Factor Weight Equipment Weight Procedure Weight

CAUSAL DISTRIBUTION HISTORIC DISTRIBUTION

HISTORIC DATA CAUSAL ANALYSIS

YES: STOP NO: ITERATE

Weighted Noise Weighted Human Factor Weighted Equipment Weighted Procedure

MIXTURE MODEL

Noise Human Factor Equipment Procedure Δ < ε ITERATION

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 12

Causes Causal Factor Distribution mean Distribution effect Noise 0,05 12,9685 0,6484 Human Factor 0,0475 8,7573 0,4159 Equipment 0,8075 10,2097 8,2444 Procedures 0,095 10,2097 0,9699

Human Factor 5% Equipment 85% Procedures 10%

CAU CAUSAL AL ANAL NALYSI SIS S FOR EAC EACH EVEN ENT TYPE COMPAR ARED WITH HIST STORIC DATA APF F PROCESS

TRA Safety Index 1 ENR Contribution Probability Relative Frequency

EVENT TYPE HISTORIC SERIES CA CAUS USAL L FI FIT T (roots ar are e fr frui uits 2/ 2/2)

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 13

GL GLOBAL L RE RESULTS (t (the he bes best is is ye yet to come

  • me)

2011 2008 2009 2010

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 14

GL GLOBAL L RE RESULTS (a (a vi view fr from af afar ar…)

SAFE AFETY INDEXES LIK LIKE PAS PAST PE PERFORMANCE EVALUATORS

Individuate cr criticalities by y X-R analysis based on Sh Shew ewart co control chart charts

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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 15

GL GLOBAL L RE RESULTS (a (a bi bit clo closer…) CA CAUS USAL ANALY LYSIS IS vs vs RE REAL L DA DATA

TRA Safety Index 1 ENR Contribution Probability Relative Frequency

0.000 0.500 1.000 1.500 2.000 2.500 3.000

SAFETY INDEX 1 ENR SAFETY INDEX 1 ENR forecast LOW limit Causal Fit UP limit Causal Fit

CA CAUS USAL vs vs TI TIME SER SERIE IES vs vs RE REAL DA DATA 3 3 meth ethodologies to FO FORECAST SAFE FETY INDEXES and thus, SAFE FETY LEV LEVELS LS

Whi Which one

  • ne is

is the the be best st one

  • ne?
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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 16

GL GLOBAL L RE RESULTS (let’s recap ap)

  • HISTORIC FIT

– Good to summarize historic behavior – Not enough accurate to forecast data

  • TIME SERIES

– Satisfactory for forecast sample short paths – Difficult mathematical implementation

CAUSAL L FIT

  • A pr

prio iori ri: Warn rning dev devic ice

– Comparison between obtained values and desired safety values

  • A po

posterio iori ri: Causal l Se Senso sor

– Analyze the events over the upper limits (unsafe) ) Evaluate the causes and take action – Analyze the events beneath the lower limits (safe) Safety’s enhancement Reduced reports – Filter our database

  • CAUSAL FIT

– Based on logic and causal analysis – Good for forecasting and monitoring – Easy implementation

CAUS USAL FIT under development with application to ENAV s.p.a. ATC:

  • Weighting procedure must be enhanced
  • Just Culture implementation to enhance reporting data quality
  • Yet possible the application to specific APT/ROUTE
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Madrid, November 26th 2014 ATM safety management: reactive and proactive indicators 17

THE THE LA LAST T SL SLIDE (you you can an cla clap you your han hands no now!!!) !)