Artificial Intelligence in Aviation Industries: Methodologies, Education, Applications, and Opportunities

Artificial Intelligence in Aviation Industries: Methodologies, Education, Applications, and Opportunities

Tetiana Shmelova (National Aviation University, Ukraine), Arnold Sterenharz (EXOLAUNCH GmbH, Germany) and Serge Dolgikh (Solana Networks, Canada)
DOI: 10.4018/978-1-7998-1415-3.ch001

Abstract

This chapter presents opportunities to use Artificial Intelligence (AI) in aviation and aerospace industries. The AI used an innovative technology for improving the effectiveness of building aviation systems in each stage of the lifecycle for enhancing the security of aviation systems and the characteristic ability to learn, improve, and predict difficult situations. The AI is presented in Air Navigation Sociotechnical system (ANSTS) because the activity of ANSTS, is accompanied by a high degree of risk of causing catastrophic outcomes. The operator's models of decision making in AI systems are presented such as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, air traffic controllers, engineers, etc. The quality of operator's decisions depends on the development and use of innovative technology of AI and related fields (Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration Decision Making, Blockchain, Artificial Neural Network, etc.).
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Background

The air transport industry plays a major role in world economic activity and to maintain the safe and efficient operation of aviation enterprises maximum use needs to be made of the enhanced capabilities provided by technical advances. Nowadays the International Civil Aviation Organization (ICAO) has added in its documents new approaches for achieving the main goals of the organization, which are: to enhance the effectiveness of global aviation security and to improve the practical and sustainable implementation of preventive aviation security measures. These new approaches include the development of security culture and human capability, the improvement of technological resources to improve oversight and quality assurance, and other directions. (ICAO, 2018). The quality of decisions dependences on the development and the use of innovative technologies in today’s aviation such including Artificial Intelligence and education of aviation operators using modern information technologies and curriculums such as AI, Data Science, Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration DM (CDM), Blockchain, Neural Network, etc.

The aviation systems should be considered as Sociotechnical systems (STS), which tend to have two common features: advanced technologies and high-risk activities (ICAO, 2004; 2013). In addition, large-scale, advanced technology systems such as Air Navigation System (ANS) can be attributed to the STS, in which the distinguishing feature is the presence of the hazardous kinds of activity as well as the use of the high-level technologies in production (Shmelova, Sikirda, Kharchenko, 2016).

Air Navigation System (ANS) is presented as an STS where Artificial Intelligence (AI) can offer significant benefits. The activity of ANS, as well as STS, is accompanied by a high degree risk of causing catastrophic outcomes. Statistical data show that human errors account for up 80% of all causes of aviation accidents (Leychenko, Malishevskiy, & Mikhalic, 2006; Campbell, Bagshaw, 2017). Since operations in STSs generally involve high-risk / high-hazard activities, the consequences of safety breakdowns are often catastrophic in terms of loss of life and property. The more a human-operator (H-O) is trying to control a production process being aided by high-level technologies, especially in case of distant operation, the more non-transparent becomes the result of the operation of a system (ICAO, 2004; 2013a; 2013a; 2017).

The document “White paper” of IATA (International Air Transport Association) presents the results of IATA research and development activities on AI in collaboration with airlines. The new technologies of AI can be clustered in the following capabilities, such as: Machine learning (ML), Natural Language Processing (NLP), Expert Systems, Vision, Speech, Planning, Robotics (IATA, 2016). So, to increase the productivity of work at each stage of the life cycle (LC) of aviation systems, different AI technologies and methods can be effectively applied (Fig. 1).

Key Terms in this Chapter

Air Navigation Socio-technical System: A complex large-scale, high-tech man-machine system, which require complex interactions between their human and technological components; the operations in socio-technical systems generally involve high-risk/high-hazard activities; the consequences of safety breakdowns are often catastrophic in terms of loss of life and property.

Markov Network: This is a graphical model in which a set of random variables possesses the Markov property described by an undirected graph. The Markov network differs from the other graphic model, the Bayesian network, the representation of the dependencies between random variables. It can express some of the dependencies that the Bayesian network cannot express (for example, cyclic dependencies); on the other hand, it cannot express some others.

Air Navigation System: A complex of organizations, personnel, infrastructure, technical equipment, procedures, rules and information that is used to provide of airspace users of safe, regular and efficient air navigation service.

Expected Aircraft’s Operating Conditions: Are the operating conditions, in which pilot’s actions are prescribing by Flight Manual.

AI (Artificial Intelligence) is the simulation of human intelligence processes by modeling: computer systems, and machines.

Non-professional Factors: Are the set of human-operator's individual-psychological, psycho-physiological and socio-psychological factors.

Unexpected Aircraft’s Operating Conditions: Are the operating conditions, in which pilot’s actions aren’t prescribing by Flight Manual.

Expert Systems (ES) -: is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.

Reflection: Is the ability of consciousness to focus on itself. The reflection of consciousness is manifested in intuitive thinking.

GERT-network (Graphical Evaluation and Review Technique): Is an alternative probabilistic method of network planning, applicable in the case when these actions can only start after completion of a prior action including cycles and loops.

Socio-Technical Systems: A large-scale, high-technology systems, because they require complex interactions between their human and technological components; the operations in socio-technical systems generally involve high-risk/high-hazard activities; the consequences of safety breakdowns are often catastrophic in terms of loss of life and property.

Professional Factors: Are the knowledge, skills and abilities, acquired by human-operator during training and professional activity accordingly.

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