=Paper=
{{Paper
|id=Vol-3101/Paper26
|storemode=property
|title=Models of decision-making by the pilot in emergency "Engine failure during take-off"
|pdfUrl=https://ceur-ws.org/Vol-3101/Paper26.pdf
|volume=Vol-3101
|authors=Tetiana Shmelova,Antonio Chialastri,Yuliya Sikirda,Maxim Yatsko
|dblpUrl=https://dblp.org/rec/conf/citrisk/ShmelovaCSY21
}}
==Models of decision-making by the pilot in emergency "Engine failure during take-off"==
https://ceur-ws.org/Vol-3101/Paper26.pdf
Models of Decision-Making by the Pilot in Emergency
“Engine Failure During Take-Off”
Tetiana Shmelova1, Antonio Chialastri2, Yuliya Sikirda3 and Maxim Yatsko4
1National Aviation University, Liubomyra Huzara ave., 1, Kyiv, 03058, Ukraine
2Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Roma RM, Italy
3Flight Academy of National Aviation University, Dobrovolskogo Str., 1, Kropyvnytskyi, 25005, Ukraine
4National Aviation University, Liubomyra Huzara ave., 1, Kyiv, 03058, Ukraine
Abstract
Timely detection of engine failure at all stages of the flight and prevention of the catastrophic
situation due to correct and coordinated collaborative actions of aviation specialists are the relevant
tasks. The general technique of decision-making by the aviation operators in emergency and diagrams
of causal relationships of the pilot actions in the case of engine failure during take-off is presented.
The flowchart of the algorithm of the pilot actions in an emergency “Engine failure during take-off”
when the captain decided to reject take-off is developed. The deterministic, stochastic, and non-
stochastic models of decision-making by the pilot in emergency “Engine failure during take-off” under
certainty, risk, and uncertainty conditions are built. The deterministic models are designed with the
help of network planning, stochastic models – on the basis of the expected value criterion with the
help of the Bayesian approach as decision tree, non-stochastic models – based on the Wald, Laplace,
Hurwitz, Savage criteria with the help of decision matrix. The worked-out models can be used both for
the informational support and professional training of the air navigation system operators.
Keywords 1
Bayesian approach, causal relationships, certainty, decision matrix, decision tree, event tree,
flowchart, network graph, risk, uncertainty
1. Introduction
Aviation is the safest mode of transport. This is a generally accepted fact, which is confirmed by
statistics. In 2014-2019, there were 107 accidents in the world, during which 3245 people died.
Whereas in 2018 alone, airlines around the world carried nearly 4.5 billion passengers on about
45 million flights [1]. 2017 was the safest year in the history of commercial airlines: a total of 10
crashes were registered, of which only half were passenger aircraft (ACFT). In 2018, according
to the Aviation Safety Network [2], the number of accidents rose sharply to 18, killing 561
people.
CITRisk’2021: 2nd International Workshop on Computational & Information Technologies for Risk-Informed Systems, September
16–17, 2021, Kherson, Ukraine
EMAIL: shmelova@ukr.net (T.Shmelova); a.chialastri@uniroma1.it (A.Chialastri); sikirdayuliya@ukr.net (Yu.Sikirda);
maxim_yatsko@i.ua (M.Yatsko)
ORCID: 0000-0002-9737-6906 (T.Shmelova); 0000-0001-5692-7161 (A.Chialastri); 0000-0002-7303-0441 (Yu.Sikirda); 0000-
0003-0375-7968 (M.Yatsko)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
ACFT crashes are very rare, about 200 times less common than car accidents. Civil aviation
statistics over the past six decades show a downward trend in tragic events and increased
security. But taking into account the registered accidents in 2019, these indicators are above the
average for the last five years [3].
The reasons for aviation accidents are human factors (68%), technical factors (18%), and
environmental factors (14%) [4–7].
2. A state-of-the-art literature review
According to a Boeing study [8], 11% of aviation accidents with human casualties occur during a
flight at cruising altitude, 2% – during the descent phase, 2% – during the initial approach to
landing, 29% – at the stage of the final approach to landing, 24% – during landing. At the
beginning of the flight, according to statistics, there are fewer problems: 12% of ACFT crashes
occur during take-off and initial climbing (before removing the flaps), 13% – during climbing
and another 7% – on the ground during towing, taxiing, loading / unloading, etc. (Figure 1).
7% 5%
Take-off
7%
Initial climbing
13% Climbing
24%
Cruising flight
Descent
Initial approach
11%
Final approach
2%
2% Landing
29% On the ground
Figure 1: Distribution of the number of ACFT crashes by flight stages
Consider how aircraft incidents are broken down by type using the statistics of the Transport
Safety Board of Canada collected from 2007 to 2017 [9] (Table 1, Figure 2).
Table 1
Distribution of incidents by types, %
Incident type
Anothe
Risk of collision / r type
Year Announcement Engine Smoke / Collisio
violation of of
of an emergency failure Fire n
intervals inciden
t
2007 19 34 15 14 1 16
2008 19 35 14 12 1 19
2009 19 40 14 12 1 14
2010 25 38 11 10 0 15
2011 18 41 14 13 1 14
2012 16 41 14 11 1 17
2013 17 42 12 10 2 17
2014 13 42 14 12 2 17
2015 14 42 14 11 1 18
2016 17 37 13 10 2 20
2017 18 37 10 11 3 21
On the
18 39 13 11 1 17
average
17% 18% Risk of collision /
violation of intervals
Announcement of an
emergency
1%
Engine failure
Smoke / Fire
11%
Collision
Another type of incident
13%
39%
Figure 2: Distribution of incidents by types, %
It can be seen that the most frequent incident is the announcement of an emergency (39%), in the
second place – the risk of collision / violation of the intervals between ACFT (18%). A
significant share is occupied by engine failure (13%), the smallest share – in collisions between
aircraft (1%).
Figure 3 shows the distribution of aviation accidents and incidents that occurred on the
territory of Ukraine in the period from 2013 to 2017 with civilian Ukrainian and foreign aircraft
by category [10].
Figure 3: Summary data of aviation accidents and incidents by categories for 2013-2017, units
This diagram indicates that incidents most often occur due to technical failures (SCF-NP), bird
collisions (BIRD), and engine failures (SCF-PP), and these trends do not change significantly
over the years. The most common causes and consequences of aviation engine failure are shown
in Figure 4 [11].
Causes Consequences
Failure of other systems of
Engine fuel system failure aircraft
Rejected take-off
Exhaust system failure
Problems with the cockpit
Failure of engine control blow-up
devices
Fuel drainage
Oil system failure
Engine failure
Deviation from the standard
departure route
Engine control system failure
Deviation from the course
Start-up system failure
Descent
Ingress of a foreign object
(bird) into the engine
Emergency landing “in front
of you”
Figure 4: Causes and consequences of aviation engine failure
The most common causes of engine failure are engine fuel system failure and exhaust system
failure. Among the consequences are the most often deviation from the standard departure route,
deviation from the course, emergency landing “in front of you” [12]. Timely detection of engine
failure at all stages of the flight and prevention of the catastrophic situation due to correct and
coordinated collaborative actions of aviation specialists are the relevant tasks.
In the works [13; 14] is provided a fragment of the network graph describing the
collaborative work of the ACFT crew (pilot-in-command – co-pilot) from the moment of engine
failure during take-off to the issuance by the captain to continue or reject take-off. The critical
time of actions of the ACFT crew and performance of works by the air traffic controller (ATCO)
in case of engine failure during take-off in deterministic and stochastic conditions is obtained.
With the help of network planning the analysis of joint actions of the ACFT crew (Pilot
Flying and Pilot Monitoring) in the case of flight emergency (FE) “Power supply problems” is
conducted, the time for operational procedures with using the method of expert assessments is
determined, structurally-time table and network graph are built, a critical time of work by two
pilots (Pilot Flying and Pilot Monitoring) is obtained [14].
Deterministic, stochastic, non-stochastic, and neural network models of the collaborative
decision-making (CDM) by ACFT pilot / unmanned aerial vehicle’s remote pilot and ATCO in
FE for maximum synchronization of operators’ technological procedures are developed [15; 16].
The purposes of this work are:
• to build models of decision-making by the pilot in the case of rejected take-off using the
example of FE “Engine failure during take-off”;
• to develop an algorithm of analysis of situation and synthesis of CDM models by the
pilot in the case of rejected take-off in FE “Engine failure during take-off”.
3. General technique of decision-making by the operators in the flight
emergency
The general technique of decision-making (DM) by the operators in FE is presented in Figure 5.
1 Analysis of FE as a 2 Building an algorithm 3 Modeling of DM by the pilot in FE:
complex situation for the pilot’s actions in - under uncertainty conditions;
(causal analysis) FE - under risk conditions;
- under certainty conditions
4 Modeling and synchronization of DM
for all CDM participants in FE:
- under uncertainty conditions;
- under risk conditions;
- under certainty conditions
5 Evaluating the effectiveness of the
decisions
Figure 5: The general technique of DM by the operators in FE
The general technique of DM by the operators in FE is included:
1. Analysis of situation as a complex situation: identification of causal relationships.
2. Building an algorithm for the pilot’s actions in FE.
3. Modeling of DM by the pilot in the case of rejected take-off as an emergency:
• models of DM under uncertainty conditions: determination of the alternatives {A} and
factors {F} that influence the choice of the optimal solution (tool – decision matrix) (Table
2);
Table 2
Decision-making matrix in uncertainty
{А} Factors influencing decision-making in emergency
f1 f2 … fj … fn
А1 U11 U12 … U1j … U1n
Alternative А2 U21 U22 … U 2j … U2n
solutions … … … … … … ….
Аi Ui1 Ui2 … U ij … Uin
… … … … … … …
Аm Um1 Um2 … U mj … Umn
• models of DM under risk conditions: evaluation of risk R for different solutions (tool –
decision tree). Each stage of DM is characterized by solutions (A = {A1; A2; …, An}), a time t
of situation development on some stage, and additional value β, that depends on the stage of
the situation development and DM in time for parry a situation (Figure 6). When solving the
problem of minimizing risks at each stage, additional risks arise (+βk), the threats are
increasing with time t (1):
𝑅𝑅𝑘𝑘 = 𝑡𝑡𝑘𝑘 ∑𝑛𝑛𝑖𝑖=1 𝑝𝑝𝑖𝑖 𝑢𝑢𝑖𝑖 ± 𝛽𝛽𝑘𝑘 ,
(1)
where ti – is a time of stage k;
βk – is an additional risk on stage k;
pi – are the probabilities of situation development, ∑𝑛𝑛𝑖𝑖=1 𝑝𝑝𝑖𝑖 = 1;
ui – are the expected outcomes (losses/profit).
The model of DM under risk is shown in Figure 6. Step-by-step correction of the decision
matrix is carried out in risk assessment [17].
Figure 6: The stages of situation development and DM in the decision tree
• models of DM under certainty conditions: determination of the optimal solution by the
criterion of minimizing the critical time of pilot actions in FE T, development of instructions
for the pilot actions in the FE (tool – network planning).
4. Modeling and synchronization of DM for all CDM participants in FE (ACFT crew,
ATCO, ground handling agents, rescue service, aerodrome service, production and dispatch
service, etc.):
• under uncertainty conditions: determination of the alternatives {A} and factors {F} that
influence the choice, determination of the optimal solution by the criterion of minimizing
potential loss U (tool – decision matrix);
• under risk conditions: determination of alternatives A and probabilities of influence the
factors P(F), determination of the optimal solution by the criterion of minimizing potential
risk R (tool – decision tree);
• under certainty conditions: determination of the optimal solution by the criterion of
minimizing the critical time of collaborative actions in the FE T, development of instructions
for joint actions of the operators in the FE (tool – network planning).
So, for example, stochastic and non-stochastic uncertainty, neural, and dynamic models can be
integrated into deterministic models. When analyzing a critical situation in a team decision (A1,
A2, A3), each operator determines his actions to solve the problem (S1, S2, and S3). In a
deterministic model some actions are ambiguous, multi-alternative (S1, S2, and S3). For
ambiguous actions, optimal solutions are found using stochastic DM models under risk or
uncertainty conditions (Figure 7).
Figure 7: The deterministic models with ambiguous actions (S1 and S2) of operators (A1, A2, A3)
After determining the minimum risks and maximum safety an integrated simplified model (S1,
S2, S3) is an aggregated deterministic model with included stochastic models (Figure 8).
Figure 8: The deterministic models with decisions A1, A2, A3
When analyzing and synthesis of situations emergency by several operators each operator
determines his actions to solve problems of ensuring the safety of flights. For example, when
need to build the CDM models for the pilot, air traffic controller, flight dispatcher, and technical
personal, for choosing optimal actions and synchronization actions of operators in the case of
rejected take-off.
5. Evaluating the effectiveness of the decisions.
Currently, the concept of Airport CDM (A-CDM) implements specific solutions that can
unite the interests of partners (airport operators, aircraft operators, ground handling agents, and
air traffic services) in joint work, to create the basis for effective DM through more accurate and
timely information that provides all partners at the airport a single operational picture of air
traffic [18–20]. The A-CDM system is expected to increase situational awareness and reduce the
risks of unauthorized ground maneuvering, and economically improve punctuality and reduce
operating costs by reducing land delays and thus saving fuel by reducing taxiing time.
4. The diagrams of causal relationships for the flight emergency
“engine failure during take-off”
Signs of engine failure during take-off are [11; 12]:
• turning the ACFT in the direction of the failed engine;
• engine pumping (clapping, shaking) and falling speed;
• increase / decrease of gas temperature behind the turbine;
• the lighting of warning devices.
Diagrams of causal relationships in the form of P-type and S-type event trees, each of which is a
branched, finite, and connected graph, which has no loops or cycles, have been developed for the
FE “Engine failure during take-off”.
The semantic model of the P-type event tree (Figure 9) includes one main event – FE, which
is combined with specific logical conditions with intermediate (branches) and initial (leaves)
prerequisites that led to its occurrence. For example, technical factors are the ingress of a foreign
object into the engine (screws, screwdrivers, small stones, birds, etc.), the destruction of the
engine shaft bearing or low-pressure turbine disk, breakage of the low-pressure compressor
working blade, gearbox failure; human factors – intentional and unintentional actions of
technical staff; environmental factors – low quality of fuel and oil, large temperature
fluctuations, etc.
The S-type event tree (Figure 10) also always uses FE as the central event, but the branches
are scenarios of FE development, and the leaves are possible consequences of its development.
Unlike an event tree of type P, an event tree of type S does not have logical nodes , .
In essence, such a semantic model is a probability graph constructed in such a way that the sum
of the probabilities of each branch is one.
Destruction of the engine shaft bearing
Destruction of the low-pressure turbine disk
Aircraft fire
Breakage of the low-pressure compressor
working blade, low-pressure turbine
Rolling of the aircraft outside the runway
Low quality of fuel and oil
Technical factors
Rejected take-off
Wear of brake mechanisms and gear tires Gearbox failure
Erroneous and untimely
actions of the aircraft crew
Failure to perform engine repair and
maintenance technologies
Violation of the diagnostics technologies of a
condition of the engine blades and disks
Poor fastening of engine disks and lack of
contouring of fasteners
Human factors
Poor fuel pipeline connection
FE “Engine failure during take-off”
FE “Engine failure during take-off”
Landing approach by the left-hand / right-hand Wildstrike
circle with a straight course
Figure 9: P-type event tree for the FE “Engine failure during take-off”
Thunderstorm activity
Figure 10: S-type event tree for the FE “Engine failure during take-off”
Landing approach by the right-hand / left-hand
turn with a reverse course
the aircraft crew Debris ingestion
Emergency landing “in front of you”
Continued take-off
Duststorm, sandstorm
Correct and timely actions of
Environmental factors
Large fluctuations of ambient temperature
5. Algorithm of decision-making by the pilot in emergency “engine
failure during take-off”
The captain is responsible for DM to reject take-off. He must decide in time to reject take-off
before ACFT reaches a DM speed V1. If a decision is made to reject the take-off, the commander
clearly declares “REJECT”, immediately commences the take-off maneuver, and resumes
control of the ACFT. If the co-pilot takes-off, he controls the ACFT until the captain positively
intervenes and takes control [21; 22].
According to the B737 Quick Reference Handbook (QRH) [22], a flowchart of the algorithm
of the pilot actions in the case of engine failure during take-off when the captain decided to reject
take-off is built (Figure 11).
Start
Lighting of warning panel
“Engine failure”
Yes No
V < V1?
Continue the
V < 80 take-off
knots?
No
Yes
Remove thrust levers to idle thrust
Disengage the autothrottles Does Yes
evacuation
Apply maximum manual braking or need?
verify operation of autobrake system
Rise speed brake lever (aerodynamic No
brake) Check brake cooling Set parking brake
schedule
Apply reverse thrust up to maximum
values depends on conditions Identify possibility to Start evacuation
vacate the runway checklist and start
Inform ATCO about take-off rejected passenger evacuation
Make sure the ACFT has stopped
Advise cabin crew to wait at their Is it possible
No
stations to vacate the
runway?
Advise cabin crew to wait at their
stations Yes
Vacate the runway Request a truck
If necessary perform memory items
Do some preventive actions
according to QRH non-normal End
checklist
Figure 11: The flowchart of the algorithm of the pilot actions in FE “Engine failure during take-
off” when the captain decided to reject take-off
Up to 80 knots, the rejected take-off is carried out in the event of [21; 22]:
• activation of the system failure alarm;
• systems failure;
• unnatural sound or vibration;
• problems with the gears;
• abnormal low acceleration during the take-off run;
• activation of incorrect take-off configuration alarm;
• fire or fire alarm actuation;
• engine failure;
• activation of the windshear warning alarm;
• involuntary opening of side windows;
• if the condition of ACFT is unsafe or impossible to take-off.
After a speed of 80 knots to a speed of V1, take-off is rejected if [21; 22]:
• fire or fire alarm actuation;
• engine failure;
• activation of the windshear warning alarm;
• if the condition of ACFT is unsafe or impossible to take-off.
During take-off, the crew member who discovers the abnormal situation will voice this as clearly
as possible.
The examples of ACFT actions in the case of rejected take-off due to FE are given in
SKYbrary [23–25].
6. Models of decision-making by the pilot in emergency “engine
failure during take-off” under uncertainty conditions
Factors influencing DM by the pilot in the FE “Engine failure during take-off”:
• f1 – the reasons for engine failure;
• f2 – ACFT flight-technical characteristics;
• f3 – ACFT equipment (manual / automatic braking systems, warning panels);
• f4 – runway tactic-technical characteristics (length, type of coverage);
• f5 – the condition of the runway surface (coefficient of adhesion);
• f6 – meteorological conditions at the aerodrome;
• f7 – category of emergency services;
• f8 – commercial factors (availability of reserve aircraft, airport fees, contracts with
handling services, etc.).
The matrix of possible results of DM by the pilot in the FE “Engine failure during take-off” is
given in Table 3.
Table 3
The decision-making matrix by the pilot in FE “Engine failure during take-off” under uncertainty
Alternative solutions Factors influencing decision-making in FE
f1 f2 ··· fj … fm
А1 Reject take-off u11 u12 ··· u1j … u1n
А2 Continue take-off u21 u22 ··· u2j … u2n
The optimal solution of DM in the FE “Engine failure during take-off” under uncertainty
conditions is determining by the Wald, Laplace, Hurwitz, Savage criteria.
7. Models of decision-making by the pilot in emergency “engine
failure during take-off” under risk conditions
Consider an example of risk calculation in the case of lighting of warning panel “Engine failure”
during take-off based on the expected value criterion with the help of the Bayesian approach,
taking into account a posteriori probabilities.
Risk function for estimating the value of average losses determined in the space of
consequences of engine parameters observations 𝑋𝑋 = |𝑥𝑥1 𝑥𝑥2 |, is set in the form (2):
𝑅𝑅 = ∑𝑥𝑥 𝑈𝑈(𝑥𝑥)(𝑌𝑌; 𝐴𝐴)𝑃𝑃(𝑥𝑥/𝑌𝑌)𝑃𝑃(𝑌𝑌),
(2)
𝑢𝑢11 𝑢𝑢12
where 𝑈𝑈 = �𝑢𝑢 � – is a payment matrix of losses incurred by the pilot as a result of
21 𝑢𝑢22
certain actions;
P(x/Y) – is a conditional distribution Х;
P(Y) – is a priori distribution Y.
The structural scheme of the DM process by the pilot in the FE “Engine failure during take-
off” in the form of a decision tree is shown in Figure 12.
P(Y1 / X) u11
A1 2
u12
P(Y2 / X)
1
P(Y1 / X)
u21
A2
3
P(Y1 / X) u22
Figure 12: The structural scheme of the DM process by the pilot in the FE “Engine failure during
take-off”
Risk in the case of DM by the pilot to reject take-off:
𝑅𝑅(𝐴𝐴1 ) = 𝑈𝑈11 �𝑃𝑃(𝑥𝑥1 / 𝑌𝑌1 )𝑃𝑃(𝑌𝑌1 ) + 𝑃𝑃(𝑥𝑥2 / 𝑌𝑌1 )𝑃𝑃(𝑌𝑌1 )� +
+𝑈𝑈12 �𝑃𝑃(𝑥𝑥1 / 𝑌𝑌2 )𝑃𝑃(𝑌𝑌2 ) + 𝑃𝑃(𝑥𝑥2 / 𝑌𝑌2 )𝑃𝑃(𝑌𝑌2 )�.
Risk in the case of DM by the pilot to continue take-off:
𝑅𝑅(𝐴𝐴1 ) = 𝑈𝑈21 �𝑃𝑃(𝑥𝑥1 / 𝑌𝑌1 )𝑃𝑃(𝑌𝑌1 ) + 𝑃𝑃(𝑥𝑥2 / 𝑌𝑌1 )𝑃𝑃(𝑌𝑌1 )� +
+𝑈𝑈22 �𝑃𝑃(𝑥𝑥1 / 𝑌𝑌2 )𝑃𝑃(𝑌𝑌2 ) + 𝑃𝑃(𝑥𝑥2 / 𝑌𝑌2 )𝑃𝑃(𝑌𝑌2 )�.
The optimal solution is an alternative with minimal risk.
The calculation of the risks of DM by the pilot in the case of engine failure during take-off is
given in Table 4. If the pilot makes a mistake of the first kind – DM to reject the take-off,
although in fact, the lighting of the warning panel has worked false – it will lead to some
economically estimated loss (flight delay). If a mistake of the second kind is made – the pilot
DM to continue the take-off, although in fact, the lighting of the warning panel has worked true
– then a catastrophe can happen. Risk of an incorrect decision, in this case, R2 >> R1.
8. Models of decision-making by the pilot in emergency “engine
failure during take-off” under certainty conditions
Based on a posteriori analysis of stochastic and non-stochastic models of DM, clarified
deterministic models are built, which serve to correct existing and develop new instructions for
pilot actions. The technology of work performance by the pilot in FE “Engine failure during
take-off” when the captain decided to reject take-off following QRH B737 is submitted in
Table 5.
Table 4
The calculation of the risks of DM by the pilot in the case of lighting of warning panel “Engine
failure”
Inputs of DM stochastic model
Engine parameters are normal (failure Probability of true lighting of
х1 P(Y1)
hypothesis false) warning panel “Engine failure”
Engine parameters are out of the Probability of false lighting of
х2 P(Y2)
norm (the hypothesis of failure is true) warning panel “Engine failure”
The probability that engine
True lighting of warning panel “Engine parameters are normal in case of
Y1 P(x1 /Y1)
failure” true lighting of warning panel
“Engine failure”
The probability that engine
False lighting of warning panel parameters are out of the norm in
Y2 P(x2 /Y1)
“Engine failure” case of true lighting of warning
panel “Engine failure”
The criterion of efficiency is the value of potential losses
Losses in case of correct actions Losses in case of incorrect actions
Losses if pilot DM to reject take-off Losses if pilot DM to reject take-off
u11 u12
(true lighting of warning panel) (false lighting of warning panel)
Losses if pilot DM to continue take-
Losses if pilot DM to continue take-off off (true lighting of warning panel)
u22 u21
(false lighting of warning panel)
Outputs of DM stochastic model
𝑅𝑅(𝐴𝐴1 ) = 𝑈𝑈11 �𝑃𝑃(𝑥𝑥1 / 𝑌𝑌1 )𝑃𝑃(𝑌𝑌1 )
R(А1) Risk if pilot DM to reject take-off + 𝑃𝑃(𝑥𝑥2 / 𝑌𝑌1 )𝑃𝑃(𝑌𝑌1 )� +
+𝑈𝑈12 �𝑃𝑃(𝑥𝑥1 / 𝑌𝑌2 )𝑃𝑃(𝑌𝑌2 ) + 𝑃𝑃(𝑥𝑥2 / 𝑌𝑌2 )𝑃𝑃(𝑌𝑌2 )�.
𝑅𝑅(𝐴𝐴1 ) = 𝑈𝑈21 �𝑃𝑃(𝑥𝑥1 / 𝑌𝑌1 )𝑃𝑃(𝑌𝑌1 )
+ 𝑃𝑃(𝑥𝑥2 / 𝑌𝑌1 )𝑃𝑃(𝑌𝑌1 )� +
R(А2) Risk if pilot DM to continue take-off
+𝑈𝑈22 �𝑃𝑃(𝑥𝑥1 / 𝑌𝑌2 )𝑃𝑃(𝑌𝑌2 )
+ 𝑃𝑃(𝑥𝑥2 / 𝑌𝑌2 )𝑃𝑃(𝑌𝑌2 )�.
Table 5
The technology of work performance by the pilot in FE “Engine failure during take-off” when
the captain decided to reject take-off [22]
№ Operation Name
1 Remove thrust levers to idle thrust a1
2 Disengage the autothrottles a2
3 Apply maximum manual braking or verify operation of autobrake system a3
4 Rise speed brake lever (aerodynamic brake) a4
5 Apply reverse thrust up to maximum values depends on conditions a5
6 Inform ATCO about take-off rejected a6
7 Make sure the ACFT has stopped a7
8 Advise cabin crew to wait at their stations a8
9 If necessary perform memory items a9
10 Do some preventive actions according to QRH non-normal checklist a10
If evacuation need
11 Set parking brake a11
12 Start evacuation checklist and start passenger evacuation a12
If evacuation does not need
13 Check brake cooling schedule a13
14 Identify possibility to vacate the runway a14
If possible to vacate the runway
15 Vacate the runway a15
If not possible to vacate the runway
16 Request a truck a16
Based on an expert’s opinion the network graph of work performance by the pilot in FE “Engine
failure during take-off” when the captain decided to reject take-off is designed (Figure 13).
a15
a11 a12 a
a10 14
a9 a16
a5 a6 a7 a8 a13
a4
a3
a2
a1
T, sec.
Figure 13: Network graph of work performance by the pilot in FE “Engine failure during take-
off” when the captain decided to reject take-off
The critical way is the operations a1–a16, located one after the other without time gaps and
overlapping. Basis on the critical way, the critical time of work performance by the pilot in FE
“Engine failure during take-off” when the captain decided to reject take-off can be determined.
9. Results
12% of ACFT crashes occur during take-off, a significant share of aviation accidents is occupied
by engine failure (13%). The most common causes of engine failure are engine fuel system
failure and exhaust system failure. Among the consequences are the most often deviation from
the standard departure route, deviation from the course, emergency landing “in front of you”.
The general technique of DM by operators in FE is included: analysis of FE as a complex
situation, construction of the algorithm of the pilot actions in FE, modeling of DM by the pilot in
FE, modeling and synchronization of DM for all CDM participants in FE, and evaluation of the
effectiveness of the decisions.
Diagrams of causal relationships in the form of P-type and S-type event trees, each of which
is a branched, finite and connected graph, which has no loops or cycles, have been developed for
the FE “Engine failure during take-off”. A flowchart of the algorithm of the pilot actions in case
of engine failure during take-off when the captain decided to reject take-off is built according to
the QRH B737.
Factors influencing DM by the pilot in the FE “Engine failure during take-off” under
uncertainty are the reasons for engine failure; ACFT flight-technical characteristics; ACFT
equipment; runway tactic-technical characteristics; condition of the runway surface;
meteorological conditions at the aerodrome; category of emergency services; commercial
factors.
An example of risk calculation in the case of the lighting of warning panel “Engine failure”
during take-off based on the expected value criterion with the help of the Bayesian approach,
taking into account a posteriori probabilities, is given.
Based on a posteriori analysis of stochastic and non-stochastic models of DM, clarified
technology and the network graph of work performance by the pilot in the case of rejected take-
off due to engine failure are submitted.
10. Conclusion
Timely detection of engine failure at all stages of the flight and prevention of the catastrophic
situation due to correct and coordinated collaborative actions of aviation specialists are the
relevant tasks. The general technique of DM by operators in FE and diagrams of causal
relationships of the pilot actions in the case of engine failure during take-off are presented. The
flowchart of the algorithm of the pilot actions in FE “Engine failure during take-off” when the
captain decided to reject take-off is developed. The deterministic, stochastic, and non-stochastic
models of DM by the pilot in FE “Engine failure during take-off” under certainty, risk, and
uncertainty conditions are built. The deterministic models are designed with the help of network
planning, stochastic models – based on the expected value criterion with the help of the Bayesian
approach as decision tree, non-stochastic models – on the basis of the Wald, Laplace, Hurwitz,
Savage criteria with the help of decision matrix.
Step-by-step correction of the decision matrix with the help of computational systems /
information technologies is carried out in risk assessment. After determining the minimum risks
and maximum safety an integrated simplified model is an aggregated deterministic model with
included stochastic models. The integration of stochastic and non-stochastic models of DM to
deterministic models based on a posteriori analysis of FE development will serve to correct
existing and develop new instructions for pilot actions. The designed deterministic, stochastic, and
non-stochastic models can be used both for the informational support and professional training of
the air navigation system operators.
The direction of further research is working out models of DM for all CDM participants within
the Airport CDM (A-CDM) concept that can unite the interests of partners (airport operators,
aircraft operators, ground handling agents, and air traffic services) in joint work, to create the basis
for effective DM through more accurate and timely information that provides all partners at the
airport a single operational picture of air traffic.
References
[1] E.Mori, Fly without fear. We answer the most common questions about airplane crashes,
2021. URL: https://suspilne.media/7904-letiti-bez-strahu-vidpovidaemo-na-najposirenisi-
zapitanna-pro-aviakatastrofi/
[2] Aviation Safety Network, 2021. URL: https://aviation-safety.net/
[3] M.Goldstein, After 900% Increase in 2018, airline fatalities rising again, 2019. URL:
https://www.forbes.com/sites/michaelgoldstein/2019/05/07/after-900-increase-in-2019-
airline-fatalities-rising-again/?sh=3d1a3c407190
[4] Aviation Risk 2020. Safety and the state of the nation, Allianz Global Corporate &
Specialty SE, Munich, Germany, 2019. URL:
https://www.allianz.com/content/dam/onemarketing/azcom/Allianz_com/press/document/Al
lianz-Aviation-Risk-2020-Report.pdf
[5] N.A.Stanton, W.-C.Li, D.Harris, Editorial: Ergonomics and human factors in aviation,
Ergonomics, vol. 62, iss. 2, 2019, pp. 131–137.
https://doi.org/10.1080/00140139.2019.1564589
[6] J.A.Wise, V.D.Hopkin, D.J.Garland (Eds.), Handbook of aviation human factors, 2nd ed.,
CRC Press, Florida, USA, 2016. doi:10.1201/b10401
[7] The main causes of aircraft crashes, RIA News, 2021. URL:
http://www.inosmi.ru/infographic/20120703/194443129.html
[8] Statistical summary of commercial jet airplane accidents. Worldwide operations 1959-2019,
Statistical summary 2019, Boeing, USA, 2020. URL:
https://www.boeing.com/resources/boeingdotcom/company/about_bca/pdf/statsum.pdf
[9] Statistical summary: Air transportation occurrences in 2017, Transportation Safety Board of
Canada, 2021. URL: https://www.bst-tsb.gc.ca/eng/stats/aviation/2017/ssea-ssao-2017.html
[10] Analysis of the state of safety of flights with civil aircraft of Ukraine based on the results of
the investigation of aviation accidents and incidents in 2013-2017, National Bureau for the
Investigation of Aviation Accidents and Incidents with Civil Aircraft, Kyiv, 2019. URL:
https://nbaai.gov.ua/wp-content/uploads/2020/05/analysis_5y.pdf
[11] Standards of flight validity of aircraft of the transport category, Aviation Rules, Part 25,
Interstate Aviation Committee, Moscow, 2014. URL: https://avia.gov.ua/wp-
content/uploads/2017/02/Aviatsijni-pravila_25.pdf
[12] S.V.Sarychev, Methodological fundamentals of technical risk assessment of the flight safety
management system in the design, production, and serial exploit of gas turbine engines,
Doctor of Technical Sciences thesis, Rybinsk State Aviation Technical University, Rybinsk,
Russian, 2017. URL: https://www.rsatu.ru/arch/diss/sarichev_sv_diss.pdf
[13] T.Shmelova, Yu.Sikirda, N.Rizun, A.-B.M.Salem, Yu.Kovalyov (Eds.), Socio-technical
decision support in air navigation system: Emerging research and opportunities, IGI Global
Publ., Hershey, USA, 2018. doi: 10.4018 / 978-1-5225-3108-1
[14] V.Kharchenko, T.Shmelova, Y.Sikirda, Decision-making in socio-technical systems:
monograph, National Aviation University, Kyiv, 2016. URL:
https://dspace.nau.edu.ua/bitstream/NAU/26332/1/%D0%9C%D0%BE%D0%BD%D0%B
E%D0%B3%D1%80%D0%B0%D1%84i%D1%8F_%D0%9F%D0%A0_%D0%A1%D0%
A2%D0%A1_2016.pdf
[15] T.Shmelova, Yu.Sikirda, M.Kasatkin, Modeling of the collaborative decision making by
remote pilot and air traffic controller in flight emergencies, In: Proceedings of the IEEE 5th
International Conference on Actual Problems of Unmanned Aerial Vehicles Developments
(APUAVD-2019), Kyiv, 2019, pp. 230–233. URL:
https://ieeexplore.ieee.org/document/8943877
[16] T.Shmelova, Yu.Sikirda, C.Scarponi, A.Chialastri, Deterministic and stochastic models of
decision making in Air Navigation Socio-Technical System, In: Proceedings of the 14th
International Conference on ICT in Education, Research and Industrial Applications.
Integration, Harmonization and Knowledge Transfer (ICTERI 2018), Kyiv, Ukraine, 2018,
Vol. II: Workshops, Part III: 4th International Workshop on Theory of Reliability and
Markov Modelling for Information Technologies (TheRMIT 2018), CEUR Workshop
Proceedings, Vol-2104, pp. 649–656. URL: http://ceur-ws.org/Vol-2104/paper_221.pdf
[17] T.Shmelova, Collaborative decision making in emergencies by the integration of
deterministic, stochastic, and non-stochastic models, chapter 10 of Information technology
applications for crisis response and management (Ed. J. W. Beard), IGI Global Publ.,
Hershey, USA, 2021, pp. 200-234. doi: 10.4018/978-1-7998-7210-8
[18] A-CDM Concept of Operations, Australia, Airservices Australia, 2014. URL:
https://www.airservicesaustralia.com/noc/cdm/docs/CDM_Concept_of_Operations_Airport
_v1.5.pdf
[19] Airport CDM Implementation: Manual, EUROCONTROL, Brussels, Belgium, 2017. URL:
https://www.eurocontrol.int/sites/default/files/publication/files/airport-cdm-manual-
2017.PDF
[20] Airport-Collaborative Decision Making: IATA Recommendations, IATA, Montreal,
Canada, 2018. URL:
https://www.iata.org/contentassets/5c1a116a6120415f87f3dadfa38859d2/iata-acdm-
recommendations-v1.pdf.
[21] Pilot’s Handbook of Aeronautical Knowledge, FAA-H-8083-25, Department of
Transportation, FAA, USA, 2016. URL:
https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/phak/media/pilot_ha
ndbook.pdf.
[22] B737 Flight Crew Operations Manual (FCOM): Quick Reference Handbook (QRH),
Boeing Company, Chicago, USA, 2018. URL:
http://www.737ng.co.uk/737NG%20POH.pdf.
[23] Rejected take off, SKYbrary, 2021. URL:
https://www.skybrary.aero/index.php/Rejected_Take_Off.
[24] B738, East Midlands UK, 2020, SKYbrary, 2021. URL:
https://www.skybrary.aero/index.php/B738,_East_Midlands_UK,_2020.
[25] Quick Reference Handbook (QRH), SKYbrary, 2021. URL:
https://www.skybrary.aero/index.php/Quick_Reference_Handbook_(QRH).