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 nonstochastic 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.
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 [
The reasons for aviation accidents are human factors (68%), technical factors (18%), and
environmental factors (14%) [
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%
7% 24% 29%
2% 2% 13% 11%
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). 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]. 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].
Consequences
aircraft 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”.
1 Analysis of FE as a complex situation (causal analysis)
for the pilot’s actions in
FE
3 Modeling of DM by the pilot in FE: - under uncertainty conditions; - 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
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.
Analysis of situation as a complex situation: identification of causal relationships. Building an algorithm for the pilot’s actions in FE.
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); • 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): = ∑ =1
± , (1) where ti – is a time of stage k; βk – is an additional risk on stage k; pi – are the probabilities of situation development, ∑ ui – are the expected outcomes (losses/profit). =1 = 1;
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]. 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). 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). 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.
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 <and>, <or>. 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.
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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). 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].
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. 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.
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 = | 1 2|, is set in the form (2): = ∑ ( )( ; ) ( / ) ( ), (2) where = 1211 1222 – is a payment matrix of losses incurred by the pilot as a result of 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 takeoff” in the form of a decision tree is shown in Figure 12.
1
A1 A2 2 3
P(Y1 / X) P(Y2 / X) P(Y1 / X) P(Y1 / X) u11 u12 u21 u22 Risk in the case of DM by the pilot to reject take-off: ( 1) = 11 ( 1 / 1) ( 1) + ( 2 / 1) ( 1) + + 12 ( 1 / 2) ( 2) + ( 2 / 2) ( 2) .
Risk in the case of DM by the pilot to continue take-off: ( 1) = 21 ( 1 / 1) ( 1) + ( 2 / 1) ( 1) + + 22 ( 1 / 2) ( 2) + ( 2 / 2) ( 2) .
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.
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. Losses if pilot DM to continue take-off (false lighting of warning panel)
u21
Outputs of DM stochastic model R(А1)
Risk if pilot DM to reject take-off R(А2)
Risk if pilot DM to continue take-off 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). 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.
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 takeoff due to engine failure are submitted. 10.
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. [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/wpcontent/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-manual2017.PDF [20] Airport-Collaborative Decision Making: IATA Recommendations, IATA, Montreal, Canada, 2018. URL: https://www.iata.org/contentassets/5c1a116a6120415f87f3dadfa38859d2/iata-acdmrecommendations-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).