53 pixels 4/4/16, 9:11 am a scientific approach for the fatigue … · 2017. 8. 21. · alfredo...

Tulio E. Rodrigues -‐ Montreal 2016 4/7/16

A Scientific Approach for the Fatigue Risk Management in the Brazilian Civil Aviation

1

Alfredo Menquini2, Arthur Lobo2, Luciano Baia4, Paulo Licati2, Philipe Pacheco4, Raul Bocces3, Tiago Bertalot3, Tiago Rosa1, Tulio Rodrigues3,5, Victor Casseta4

1Crew Member National Union - SNA 2Brazilian Civil Aviation Pilots Association - ABRAPAC

3Gol Airline Crew Member Association - ASAGOL 4TAM Airline Crew Member Association - ATT

5Physics Institute, University of São Paulo

In partnership with:

4/4/16, 9:11 AMfrmsforum_logo.jpg 248×53 pixels

Página 1 de 1http://137.117.64.219/www.icao.int/safety/fatiguemanagement/frms2011/publishingimages/frmsforum_logo.jpg

Tulio E. Rodrigues -‐ Montreal 2016 4/7/16 2

1) Overview of the SAFTE-FAST model

2) Results:

•  Pilot fatigue in Brazil

•  Risk analyses: scenarios, fatigue risk & risk exposure

•  FDT Limits: a science based proposal for minimum crew

3) Conclusions

4) Future plans

Outline




(*) Adapted from S. Hursh et al., Aviat Space Environ Med 2004; 75(3 Suppl): A44-53

Homeostatic Process

Circadian Rithmin

Sleep Inertia

Effectiveness ~ (Reaction Time)-1 Hours awake

Reservoir ê Hours of sleep

Reservoir é

Sleep reservoir

Sleep Intensity

Overview of the SAFTE-FAST model*




Results/pilot fatigue in Brazil

(*) Licati, PR; Rodrigues, TE; Wey, D; Fischer, FM; Menna-Barreto, L. (2015) Conexão Sipaer, Vol. 6, No. 1, pp. 7-17.

14

40 50 60 70 80 90 1000

10

20

30

40

50

60

E

vent

s

Effectiveness (%)

This work (Licati et al., 2015) Gaussian FIT

Figure 4: Distribution of pilot effectiveness at the time of fatigue sensation

according with the SAFTE-FAST predictions (Licati et al., 2015).

Combining the information presented in figures 4, 5 and 6 one can

easily verify that the sample of pilots that was studied presented

low levels of effectiveness with almost 50% of the reports

concentrated at 10:00 in the morning and with a very small

average wakefulness of 7 hours. In order to provide a suitable

explanation for this apparent contradiction, the researchers

decided to investigate the correlation between the time of the

fatigue sensation (clock time) and the wake-up time (start of

wakefulness).

Average effectiveness: 73.8 ± 0.8% (E = 77.5% à BAC ≈ 0.05%)

Pilot Effectiveness*




Pilot fatigue in Brazil

WOCL Shoulder…

Fatigue clock time - FT

0 2 4 6 8 10 12 14 16 18 20 22 240

10

20

30

40

50

60

70

80

8%49%43%

Even

ts This work Sum of three gaussian shapes <FT>1 = 3.1 ± 0.4 (h) <FT>2 = 9.8 ± 0.5 (h) <FT>3 = 21.3 ± 0.4 (h)

Fatigue "clock time" (h)





15

0 2 4 6 8 10 12 14 16 18 20 22 240

10

20

30

40

50

60

70

80

8%49%

43%

Eve

nts

This work Sum of three gaussian shapes <FT>

1 = 3.1 r 0.4 (h)

<FT>2 = 9.8 r 0.5 (h)

<FT>3 = 21.3 r 0.4 (h)


Figure 5: Distribution of fatigue reports as a funtion of the time of the day.

0 5 10 15 20 25 300

20

40

60

80

100

47%53%

Wakefulness prior to fatigue sensation (h)

Eve

nts

This work Sum of two gaussian shapes <AT>

1 = 7.0�r�0.6 (h)

<AT>2 = 18.0 r 0.5 (h)

Figure 6: Distribution of wakefulness prior to fatigue sensation.

7h? 53% of the pilots

manifested fatigue after

only 7 hours of wakefulness

Wakefulness - AT




Pilot fatigue in Brazil 18

2 3 4 5 6 7 8 90

20

40

60

80

100

E

ven

ts

Pilot reports Gaussian Fit (average sleep of 5.05 h)

Amount of sleep in the last 24 hours (h)

Figure 9: Distribution of the amount of sleep reported by the pilots within the

last 24 hours prior to the fatigue sensation.

4 6 8 10 120

20

40

60

80

100

E

ven

ts

FAST (301 fatigue reports) Sum of two gaussian shapes Average value = 7.41 r 0.22 (h)

Chronic sleep debt in the last 72 hr (h)

Figure 10: Distribution of chronic sleep debt within the last 72 hours prior to

the fatigue sensation.

18

2 3 4 5 6 7 8 90

20

40

60

80

100

E

ven

ts

Pilot reports Gaussian Fit (average sleep of 5.05 h)

Amount of sleep in the last 24 hours (h)

Figure 9: Distribution of the amount of sleep reported by the pilots within the

last 24 hours prior to the fatigue sensation.

4 6 8 10 120

20

40

60

80

100

E

ven

ts

FAST (301 fatigue reports) Sum of two gaussian shapes Average value = 7.41 r 0.22 (h)

Chronic sleep debt in the last 72 hr (h)

Figure 10: Distribution of chronic sleep debt within the last 72 hours prior to

the fatigue sensation.

• Insufficient sleep in the last 24 hours (5.05 h) and • chronic sleep debt in the last 72 hours (7.4 h)

Sleep history




Mean values (h)

Wakefulness Fatigue Time

18.0 ± 0.5 3.1 ± 0.4 7.0 ± 0.6 9.8 ± 0.5

Pilot fatigue in Brazil 15

0 2 4 6 8 10 12 14 16 18 20 22 240

10

20

30

40

50

60

70

80

8%49%

43%

Eve

nts

This work Sum of three gaussian shapes <FT>

1 = 3.1 r 0.4 (h)

<FT>2 = 9.8 r 0.5 (h)

<FT>3 = 21.3 r 0.4 (h)


Figure 5: Distribution of fatigue reports as a funtion of the time of the day.

0 5 10 15 20 25 300

20

40

60

80

100

47%53%

Wakefulness prior to fatigue sensation (h)

Eve

nts

This work Sum of two gaussian shapes <AT>

1 = 7.0�r�0.6 (h)

<AT>2 = 18.0 r 0.5 (h)

Figure 6: Distribution of wakefulness prior to fatigue sensation.

15

02

46

810

1214

1618

2022

2401020304050607080

8%49

%

43%

Events

Thi

s wor

k Su

m of

thre

e gau

ssian

shap

es <

FT> 1 =

3.1 r

0.4

(h)

<FT

> 2 = 9.

8 r 0

.5 (h

)

<FT

> 3 = 21

.3 r

0.4 (

h)

Fatig

ue "c

lock

tim

e" (h

)

Figu

re 5:

Dist

ribut

ion

of fa

tigue

repo

rts as

a fu

ntio

n of

the t

ime o

f the

day.

05

1015

2025

30020406080100

47%

53%

W

akefu

lnes

s prio

r to f

atigu

e sen

satio

n (h

)

Events

Thi

s wor

k Su

m of

two g

auss

ian sh

apes

<AT

> 1 = 7.

0�r�0

.6 (h

)

<AT

> 2 = 18

.0 r

0.5 (

h)

Fi

gure

6: D

istrib

utio

n of

wak

efuln

ess p

rior t

o fat

igue s

ensa

tion.

Fatigue time x wakefulness

0 5 10 15 200

5

10

15

20

25

30

Fatigue Time, FT (h)

Wak

eful

ness

, AT

(h) Fatigue Reports





16

Figure 7 shows the fatigue clock time as a function of the wake-up

time for all the data, where it is clear two quite distinct behaviors.

The left side plot shows the fatigue events that took place one day

after the start of wakefulness, while the right hand plot presents

the events that occurred during the same day of the awakening. In

the first case it is clear that the fatigue was felt essentially around

04:00 and almost independent of the start of wakefulness. In the

second case, there is a strong variation in the fatigue time with the

wake-up time. Figure 8 presents an exponential fitting (solid red

line) with its respective limits (dashed blue lines) obtained via the

propagation of uncertainties of the fitted parameters (Licati et al., 2015).

-24 -20 -16 -12 -8 -4 0 4 8 12 16 20 240

4

8

12

16

20

24

51%49%

day beforefatigue sensation

same day offatigue sensation

Data points

Wake-up time

Fat

igu

e cl

ock

-tim

e

Figure 7: Fatigue reported clock-time versus wake-up time.

17

0 4 8 12 16 20 240

4

8

12

16

20

24

Wake-up time

day beforefatigue sensation

Data points Exponential Fit Statistical limits

4 8 12 16 20 24

Fat

igu

e cl

ock-

tim

e

same day offatigue sensation

Figure 8: Relationship between the wake-up time and the fatigue clock-time

(solid squares). The solid red line represents the exponential fitting with its

respective statistical limits shown by the dashed blue lines.

This result demonstrates the contribution of two distinct effects.

The first one is related with the higher probability of fatigue

reports during the WOCL and is almost independent of the wake-

up time. The second effect is probably related with the rostering

structure that generates a progressive sleep debt in consecutive

early starts duties without adequate sleep opportunity. The latter

can be further verified by the inspection of the amount of sleep

reported by the pilots within the last 24 hours prior to the fatigue

sensation (Figure 9), as well as with the analysis of the chronic

sleep debt accumulated in the last 72 hours (Figure 10).

Exponential relationship between FT and AT

Fatigue time x awake time




Scenario 1: first late night duty*

26

x Intrinsic software commute 4: zero

x Minimum sleep: 60 minutes

The following tables present all the scenarios and initial conditions

studied in this work.

Scenario 1: Crew member checks in fully recovered

Check-in (h) ' = 2h ' = 3h Duty time

(h) # results

02:00 M1 M3 10 2 04:30 M5 M7 10 2 05:30 M9 M11 12 2 12:30 M13 M15 13 2 14:30 M17 M19 12 2 15:00 M21 M23 12 2 15:30 M25 M27 11 2 19:30 M29 M31 12 2 22:30 M33 M35 11 2 23:30 M37 M39 10 2

Table 3: Initial conditions adopted in the present analysis for a crew member

commencing the duty period fully recovered. A portion of the flight can elapse

between 0000 and 0600 in this scenario.

According with table 3, two different wakefulness prior to check-in

were considered, one with 2 hours (' = 2h) and another one with

three hours (' = 3 h). The labels M1, M3, M5, etc... refer to the

respective initial condition and are used for notation purposes

only.

4 The commute in the FAST software was set to zero since the simulations already assume pre‐defined and realistic wakefulness hypotheses by the check‐in time.

Δ: elapsed time between wake-up and check-in

(*) “late night duty” indicates that any fraction of the duty can elapse between 0000 and 0600

Risk Analyses




27

Scenario 2: Crew member starts the second work day after completing the scenario 1 in the first day

Check-in (h) ' = 2h ' = 3h Duty period (h)

# results


Table 4: Initial conditions for the crew member that starts the second day

after completing the first day in the scenario 1. A portion of the flight can

elapse between 0000 and 0600.

Scenario 3: Crew member checks-in fully recovered

Check-in (h) ' = 2h ' = 3h ' = 4hDuty

period (h)# results

06:30 M41 M43 -- 13 2 07:30 M45 M47 -- 14 2 09:30 M49 M51 M53 14 3

Table 5: Initial conditions for a crew member commencing the duty period

fully recovered. No portion of the flight elapses between 0000 and 0600.

These are typical early-starts conditions.

Scenario 2: second late night duty Risk Analyses




27

Scenario 2: Crew member starts the second work day after completing the scenario 1 in the first day

Check-in (h) ' = 2h ' = 3h Duty period (h)

# results


Table 4: Initial conditions for the crew member that starts the second day

after completing the first day in the scenario 1. A portion of the flight can

elapse between 0000 and 0600.

Scenario 3: Crew member checks-in fully recovered

Check-in (h) ' = 2h ' = 3h ' = 4hDuty

period (h)# results

06:30 M41 M43 -- 13 2 07:30 M45 M47 -- 14 2 09:30 M49 M51 M53 14 3

Table 5: Initial conditions for a crew member commencing the duty period

fully recovered. No portion of the flight elapses between 0000 and 0600.

These are typical early-starts conditions.

1st day 28

Scenario 4: Crew member in the third consecutive day after the completion of two successive early-starts

Check-in (h) ' = 2h ' = 3h ' = 4hDuty time

(h) # results

06:30 M42A M44A -- 13 2 07:30 M46A M48A -- 14 2 09:30 M50A M52A M54A 14 3

Table 6: Initial conditions for a crew member in the third consecutive day

after the completion of two successive early-starts.

Scenario 5: Crew member in the sixth consecutive day after the completion of five successive early-starts

Check-in (h) ' = 2h ' = 3h ' = 4h Duty time (h) # results

06:30 M42 M44 -- 13 2 07:30 M46 M48 -- 10 2 09:30 M50 M52 M54 12 3

Table 7: Initial conditions for a crew member in the sixth consecutive day after

five successive early-starts.

The simulations were carried out entirely by the IBR team for all

the initial conditions depicted in Tables 3 to 7. The plots and the

results were gently provided by Lauren Waggoner, PhD (IBR) and

are presented below in Figures 12 to 17.

28

Scenario 4: Crew member in the third consecutive day after the completion of two successive early-starts

Check-in (h) ' = 2h ' = 3h ' = 4hDuty time

(h) # results

06:30 M42A M44A -- 13 2 07:30 M46A M48A -- 14 2 09:30 M50A M52A M54A 14 3

Table 6: Initial conditions for a crew member in the third consecutive day

after the completion of two successive early-starts.

Scenario 5: Crew member in the sixth consecutive day after the completion of five successive early-starts

Check-in (h) ' = 2h ' = 3h ' = 4h Duty time (h) # results

06:30 M42 M44 -- 13 2 07:30 M46 M48 -- 10 2 09:30 M50 M52 M54 12 3

Table 7: Initial conditions for a crew member in the sixth consecutive day after

five successive early-starts.

The simulations were carried out entirely by the IBR team for all

the initial conditions depicted in Tables 3 to 7. The plots and the

results were gently provided by Lauren Waggoner, PhD (IBR) and

are presented below in Figures 12 to 17.

3rd day

6th day

Scenarios 3, 4 & 5: early-starts Risk Analyses




Data points: S. Hursh, T. Raslear, A. Kaye and J. Fanzone, Jr. (2006). Validation and Calibration of a Fatigue Assessment Tool for Railroad Work Schedules, Summary Report. (Report No. DOT/FRA/ORD-06/21). Washington, DC: U.S. Department of Transportation.

9

The results are presented in Figure 3, together with the fitted

function2 R(E) = a + b/E, with a= 0.27 ± 0.20, b = 0.58 ± 0.19 (F2

= 3.03 and n.d.f. = 3).

40 50 60 70 80 90 100

0.8

1.2

1.6

2.0H

F a

ccid

en

t rela

tiv

e l

ikeli

ho

od

SAFTE-FAST predicted effectiveness (%)

Hursh (2006)

Fitted function (~1/E)

Figure 3: Human-Factor (HF) accident relative likelihood as a function of

SAFTE-FAST predicted effectiveness. Details in the text.

The data of Figure 3 (Hursh et al., 2006) represent the relative

probability of accidents caused by human errors normalized by the

amount of work hours (exposure). The error bars were estimated

to be N1/2, where N is the number of total accidents for a given

effectiveness interval.

The risk exposure was calculated as a function of the area of the

effectiveness curve along the duty period below a threshold value,

herein fixed at 80%. This limit is close to the level of 77%, which

corresponds to a blood alcohol concentration (BAC) of 0.05% (the

2 For the statistical analyzes we have adopted the Least Squares Method described elsewhere (Helene, 2013).

Fatigue risk versus Effectiveness Risk Analyses




Risk Analyses 32

with the original histogram from the IBR team. The average

effectiveness for this initial condition was 78.33%.

2 4 6 8 10 1250

60

70

80

90

100

Eff

ecti

ven

ess

(%)

Time of the day (h)

M1 (Eave

= 78.33%)

Interpolation

Figure 18: Interpolation of SAFTE-FAST predicted effectiveness during the

first night shift with check-in at 0200 (M1). The average effectiveness Eave =

78.33% was calculated by the integral of E(t) from the check-in until the

check-out.

Using the relationship presented in Figure 3 one can estimate the

risk due to fatigue along the duty period. The result for M1 is

shown in Figure 19.

In the next step we evaluate the hazard area (HA) below our

threshold value of effectiveness ETh=80%. As seen by the

inspection of Figure 20, the total costs for human-factor accidents

vary quite significantly when the effectiveness drops below 77%,

supporting our strategy to map the area below 80%.

33

2 4 6 8 10 120.92

0.96

1.00

1.04

1.08

1.12

M1: <RM1

> = 1.015

R(E) ~1/E

Rela

tiv

e F

ati

gu

e R

isk

Time of the day (h)

Figure 19: Relative fatigue risk during the first night shift with check-in at

0200 (M1). The average relative risk of 1.015 was calculated by the integral of

R(t).

Figure 21 shows a comparison between the first and the second

night shifts (M1 and M2) with its respective hazard areas, where

one clearly verifies an increase of almost a factor of five in the risk

exposure comparing the first and the second night shift (M2/M1 ~

5). Table 8 summarizes all the results obtained for the six scenarios

and more than 60 different initial conditions.

(*) SAFTE-FAST predicted Effectiveness were generously provided by Lauren Waggoner from IBR. The plots refer to the first night duty with check in at 02:00.

Effectiveness* à Risk

Eave = 78.33 % <R> = 1.015




Risk Analyses

34

Figure 20: Total cost of human-factor accidents as a function of SAFTE-FAST

predicted effectiveness. The plot was extracted from Hursh et al. (2011).

2 4 6 8 10 120

20

40

60

80

100

HA ratio between M2 and M1: 4.7

Time of the day (h)

Eff

ecti

ven

ess

(%)

M1 (HA = 0.265 h) M2 (HA = 1.246 h) Threshold value of 80%

Figure 21: Comparison between the hazard areas (HA) obtained in the first

(M1) and in the second (M2) night shifts with check-in at 0200. Details in the

text.

The hazard areas (HA) were calculated by the fraction of the duty period with SAFTE-FAST Effectiveness below a threshold of 80%.

Effectiveness & Hazard Area (HA)

5HAHA

M1

M2 ≈




Risk Analyses 37

0.8 0.9 1.0 1.1 1.2 1.3 1.40

1

2

3

4

Eave

= 77%Eave

= 90%

Low Risk Zone (HA = 0 and Eave

t 90%)

Caution Zone (77% d�Eave

< 90%)

Danger Zone (Eave

< 77%)

Haz

ard

Are

a, H

A (

h)

Average Relative Risk

Figure 23: Relationship between the average relative risk and the hazard area

(HA) for all the initial conditions considered in the simulations. Details in the

text.

Obviously that the linear relationship between <R> and HA for the

average effectiveness below 77% reflects our choice for the

threshold value of 80% to calculate the areas. It is worth-

mentioning, however, that this value is not supposed to provide a

go no go decision, but to establish a reference limit where the risk

exposure starts to build up.

So, in order to verify that the proposed method is in fact useful for

risk mitigation, we have included in the present analysis two

famous fatigue-related accidents that were largely studied with the

help of the SAFTE-FAST model. The first was the AIA 808 that

crashed in Guantanamo Bay, Cuba in 1993 and the second one was

the Comair 5191 (2006) in Lexington, USA.

Risk exposure: average risk versus HA

GREEN: LOW RISK

AMBER: CAUTION

RED: DANGER




Risk Analyses

41

undoubtedly that this parameter has to be taken into account very

carefully during the risk analysis and mitigation.

0.8 0.9 1.0 1.1 1.2 1.3 1.40

1

2

3

4

<E> = 77%<E> = 90%

Low Risk Zone Caution Zone Danger Zone AIA 808 - Captain AIA 808 - First Officer AIA 808 - Flight EngineerComair 5191 - Air Traffic Controller

Haz

ard

Are

a, H

A (

h)

Average Relative Risk

Figure 26: Relationship between the average relative risk and hazard area

obtained for the 61 initial conditions of this work, together with the results

obtained for actual accidents in Guantanamo Bay (blue symbols) and

Lexington (magenta square).

SAFTE-FAST Effectiveness calculations: (1) AIA 808 (Guantanamo Bay): Wesensten & Belenky (private comm.)

(2) Comair 5191: Pruchnicki, Wu & Belenky (2011)

Risk exposure: including real accidents (1,2)




FDT Limits A science based proposal for minimum crew

Start with CAO’s-48 (scenarios 2 and 4), but limited to 12 hours*

(*) Details at http://www.asagol.com.br/files/_dirtecnica/FRMS/FRMS%20Report%20Part%20II.pdf

Duty &me limits (1 or 2 sectors) in scenarios 2 and 4* with Δ = 2 hours

Check-‐in (h) Ini?al condi?on label

Maximum duty ?me (h)

Average risk Hazard area (h)

0500-‐0559 M10 11 1.059 0.667

0600-‐0659 M42A(*) 12 1.089 0.974

0700-‐0759 M46A(*) 13 à 12 0.973 0

0800-‐1059 M50A(*) 14 à 12 0.894 0

1100-‐1359 M14 13 à 12 0.871 0

1400-‐1459 M18 12 0.869 0

1500-‐1559 M26 11 0.869 0

1600-‐2259 M30 10 0.978 0.174

2300-‐0459 M2 10 1.135 1.246





Current (Federal Law 7.183/84) versus “future” limits (one or two sectors)

0 2 4 6 8 10 12 14 16 18 20 22 246

7

8

9

10

11

12

13

14

D

uty

time

limit

(h)

Check-in time (h)

HA = 0.174

HA = 0HA = 0.974

HA = 0.667

HA =1.246

Federal Law 7.183/84





Safety x Productivity Fatigue Risk As Low As Reasonably Practicable (ALARP)

0 2 4 6 8 10 12 14 16 18 20 22 246

7

8

9

10

11

12

13

14

D

uty

time

limit

(h)

Check-in time (h)

HA = 0.174

HA = 0HA = 0.974

HA = 0.667

HA =1.246

Federal Law 7.183/84

Red zone: FDT reduced by 1 hour

Amber zone: FDT maintained

Green zone: FDT increased to 12 hours





Duty and flight time limitations

Check-in (h) Number of flight sectors

1-2 3-4 5 6 7+ 0500-0559 10(9) 9(8) 9(8) 9(8) 9(8) 0600-0659 11(9) 10(9) 9(8) 9(8) 9(8) 0700-0759 12(9.5) 12(9) 11(9) 11(9) 10(8) 0800-1059 12(10) 12(9.5) 12(9) 11(9) 11(9) 1100-1359 12(9.5) 12(9) 11(9) 11(9) 10(8) 1400-1459 12(9) 11(9) 10(8) 10(8) 9(8) 1500-1559 12(9) 11(8) 11(8) 10(8) 10(8) 1600-2259 10(8) 9(8) 9(8) 9(8) 9(8) 2300-0459 9(8) 9(8) 9(8) 9(8) 9(8)

ü  Limits in black: the same as CAO’s-48, but limited to 12 hours ü  Limits in red: one hour lower than CAO’s-48 ü  Limits in blue: one hour higher than CAO’s-48



Tulio E. Rodrigues – Montreal 2016 4/7/16 22

Conclusions

1.  Licati et al. (2015) found a scenario of chronic fatigue in Brazil with an average effectiveness of 73.8 ± 0.8 %. Roma et al. (2012) found a much higher value of 87.9% in USA. Comparing these experiments, the average fatigue risk in Brazil is 14% higher than in the USA.

2.  More than 50% of the pilots manifested fatigue after only 7 hours of wakefulness. This result was explained by an insufficient sleep in the last 24 hours (average of 5 h) and chronic sleep debt in the last 72 hours (average of 7.4 h).

3.  Single days-off should be avoided as they do not allow for a complete recovery. If they are needed it is not recommended that the crew members show to work before 10:00 in the next day.




Conclusions

4.  The Hazard Areas (HA) in the second late night duty (CAO’s-48 or FAR’s-117 FDT limits) is roughly 5 times higher than in the first one;

5.  The HA during the third successive early-start before 07:00 is similar to the one found in the second late night duty;

6.  The daily averaged HA of FAR’s-117 is 26% (9%) higher than CAO’s-48 in scenarios 1 and 3 (2 and 5);

7.  The relative human-factor accident risk increases by 45% (90%) as the time on duty increases from 10 (8) to 12 hours [(Goode, 2003), reanalysis]. Is it really necessary to go beyond 12 hours for minimum crew?




Conclusions

I.  The importance of an effective participation of Crew Member Representatives in de decision making of the FSAG;

II.  Review of Prescriptive Limits as they play a major role for the fatigue risk management (ICAO’s Annex 6). A step back may be needed;

III. The State FRMS Regulations can not rely 100% in a “copy

and paste” strategy.

Lessons learned




Future Plans

1.  Member Institutions: SNA, ABRAPAC, ASAGOL and ATT

2.  Objective: To monitor the alertness level of the Brazilian crew members during their duty periods, providing data analyses for the identification of potential hazards, as well as safety recommendations for risk mitigation

3.  Partnerships with IFALPA (HUPER Committee), Universities, Research Centers, FRMS Specialists, etc…

4.  When to start: hopefully by the end of 2016!




Acknowledgments

v  Crew Member National Union (SNA) v  Brazilian Civil Aviation Pilots Association (ABRAPAC) v  GOL Airline Crew Member Association (ASAGOL) v  TAM Airline Crew Member Association (ATT) In Partnership with: q  University of São Paulo (USP) q  Institutes for Behaviour Resources, Inc Collaborating Stakeholders: ü  National Centre of Investigation and Prevention of Aeronautical

Accidents (CENIPA) ü  National Commission of Human Fatigue (CNFH) Special Thanks to: Steven Hursh (IBR), Lauren Waggoner (IBR), Reid

Blank (IBR), Gregory Belenky (WSU) and Nancy Wesensten (WRAIR)

Thank you!



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