Álvaro Rodríguez Sanz

By Rosa Arnaldo Valdés, Víctor Fernando Gómez Comendador, Alvaro Rodriguez Sanz and Javier Perez Castán

The world is talking about the Industry 4.0 or the fourth industrial revolution, that is, the current trend of higher level of automation, digitalization and data exchange in manufacturing technologies. It includes cyber-physical systems, Internet of Things and cloud computing among other technological assets. With more than 5000 sensors, which generate up to 10 GB of data per second, modern aircraft engines are an exponent of what digitalization and the Internet of Aircraft Things could furnish, as part of the upcoming Industry 4.0 revolution, in the aviation industry. This new era has the potential to improve air transport key performance areas. Particularly, in an industry where safety levels are so high and the margins for improvement are extremely tight, this upcoming era might imply a shift in safety improvement. In an attempt to define Aviation 4.0, this chapter discusses the stages of aviation development from basic VFR flight rules at Aviation 1.0 up to Aviation 4.0 where cyber-physical systems are designed to assist humans’ unkind or hazardous work, to take decisions and to complete tasks autonomously. It illustrates the current and future cases of application of Aviation 4.0 to increase the aviation safety, while outlines how they might increase aviation safety levels.

Part of the book: Aircraft Technology

By Rosa Maria Arnaldo Valdés, V. Fernando Gómez Comendador, Alvaro Rodriguez Sanz, Eduardo Sanchez Ayra, Javier Alberto Pérez Castán and Luis Perez Sanz

Most decisions in aviation regarding systems and operation are currently taken under uncertainty, relaying in limited measurable information, and with little assistance of formal methods and tools to help decision makers to cope with all those uncertainties. This chapter illustrates how Bayesian analysis can constitute a systematic approach for dealing with uncertainties in aviation and air transport. The chapter addresses the three main ways in which Bayesian networks are currently employed for scientific or regulatory decision-making purposes in the aviation industry, depending on the extent to which decision makers rely totally or partially on formal methods. These three alternatives are illustrated with three aviation case studies that reflect research work carried out by the authors.

Part of the book: Bayesian Networks

By Javier Alberto Pérez-Castán and Alvaro Rodríguez-Sanz

Remotely Piloted Aircraft Systems (RPAS) are new airspace users that require to be safely integrated into the non-segregated airspace. Currently, their integration is planned for the horizon 2025, but there is a lot of pressure by RPAS operators to fly as soon as possible. This research focuses on the development of a risk-based framework for the integration of RPAS in non-segregated airspace. The risk-based framework relies on a hierarchical methodology that is split into two time horizons: design and operation. Different operational and geometrical factors characterise each stage. Then, a set of risk and operational indicators are defined for each stage. These indicators evaluate the operational airspace state and provide information about how the integration of RPAS should be. Primary results provide information about geographical and temporary restrictions. Geographical restrictions refer to the airways that favour or inhibit the integration of RPAS, and temporary restrictions denote the time span when the RPAS can pierce into the airspace.

Part of the book: Risk Assessment in Air Traffic Management