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International audience; In the beginning of 2022, the world is still fighting the crisis caused by the COVID-19 outbreak. The scientific community is still dedicating significant efforts to identify which are the better strategies to mitigate the pandemic and establish how and when to apply them. Modeling and simulation are a common method to replicate and foresee the behavior of the epidemic curve, but traditional analytical models are not capable to explain and reproduce the real evolution of the number of infections and deaths as they only concentrate in the epidemiological aspects of the virus. The COVID-19 crisis has an impact in all fundamental levels of society, and this is the reason why its modeling requires a global perspective and a holistic approach. Though the engineering scope is not common in the study of public health crises, this paper concludes that some engineering tools such as systems analysis and control theory may be the answer to build a high-fidelity model to support the decision-making facing the emergency.

Over the past two decades, the promoters of Model Based Systems Engineering (MBSE) have encouraged systems engineers to transition from document-centric approaches to model-based ones. Literacy of systems engineers in reading, sharing and elaborating models has therefore become an issue. Whatever the modeling language, elaboration of models is a highly complex intellectual process and SysML is no exception. Feedback from industry practitioners and MBSE lecturers suggests that developers of SysML models often stumble on the same problem: thinking about the system before modeling it in SysML. The authors of this paper propose to ease that elaboration process by using mind maps. With their graphic form and rather flexible way of organizing ideas, mind maps turn out to be a good candidate to help thinking about the system. Unlike approaches that directly switch from mind maps to SysML diagrams dedicated to one specific system, this paper introduces an intermediate step: mind maps first enable elaboration of diagrams patterns. The latter may in turn be instantiated onto one or several systems. Without loss of generality, the proposed approach is step-wise illustrated on real-time systems monitored by software controllers. Patterns are proposed to cover need expression, requirement capture, use case driven analysis and design.

Innovation is a vital element in the dynamic world we live in, aiming at add-ing value to developing new systems. Obsolescence on the other hand com-prehends the deterioration of the system or component capacity to operate when it is no longer suitable to fulfil the required function, even if it still operates and can be manufactured and supported. As innovative and obsolete products can be seen as opposites, the proactive management of obsoles-cence encompasses the principles of innovation. Obsolescence has so far been mainly addressed from a reactive point of view. Realizing the impact of the conceptual design phase on a system’s life-cycle cost, this paper presents how a model-based system engineering approach, proactively assessing obso-lescence risks, leverages with innovation considerations. In doing so, the pa-per builds on technology readiness levels (TRL) to identify critical compo-nents and innovation considerations. With clearly identified risks, designers can knowingly decide whether to improve a design, by making it robust and resilient, or to accept the risks and develop an obsolescence management plan. The paper is illustrated with an example from the automotive industry.

COVID-19 recently grew as a major problem worldwide and is considered the pandemic of the third century. Among the issues in dealing with this pandemic is the urgency, the bad communication, the decentralization of decision and the mismanagement of this crisis. In order to address these issues, the authors propose the use of Internet of Things and Artificial Intelligence. The Internet of Things proposes several solutions to the aforementioned problems, while using the Genetic Algorithm in AI to calibrate the proposed generalized SEIR mathematical model. This model, when calibrated to the existent recorded and confirmed data in Lebanon, will help predict the number of infected individuals. Thus, it will better prepare for the fight with the virus.

Aircraft systems and associated technologies have been con- stantly evolving over the years with an increasing number of components and actors interacting together in a global chain. Given this ever-growing complexity, the use of simulation in the design and validation phases of emerging systems is essential and allows to significantly accelerate some prototyping phases, especially in cross-domain concepts. This article de- scribes analysis and experiments done at ISAE-SUPAERO for running large scale and hybrid real-time simulations for aircraft using the HLA standard (High Level Architecture).

Case-Based Reasoning (CBR) allows emulating the human inference of solutions to problems profiting from previous experience. The integration of CBR with ontologies, structured organization of semantic knowledge, has been in the attention for some time, aiming to create powerful knowledge-intensive systems capable of proposing appropriate solutions to problems. This entails having collected an appropriate number of previous cases as well as having established a suitable ontology for the application domain. This paper focuses on the integration of CBR and ontologies to support the case representation, case base storage, and semantic similarity estimation. Different alternatives for such integration are explored and the approach has been tested in the creation of a Decision-Support System for the design of predictive maintenance systems. This work opens the window to significantly improve the capabilities of a CBR system by using the knowledge materialization and the reasoning features of a specific domain ontology.

Obsolescence is the fact that an entity (physical or logical) is becoming outdated or no longer useful and in this way is considered inappropriate. The objectives of this article are twofold. First, we seek to contribute to the understanding of obsolescence through two conceptual models. They enable, among others, to link the points of view of the external and internal actors of the system. This mapping is fundamental when it comes to solve the re-design problems posed by some obsolescence issues. The obsolescence management process consists of Prepare, Identify, Assess, Analyze, and Implement. The second objective is to offer two support tools for the Identify and Assess phases: House of Obsolescence and System Obsolescence Exposure Analysis. The first allows to map the changes, desired or imposed, by external actors to the critical components and functions of the system architecture. These latter are then analyzed using the second tool whose objective is to assign an obsolescence exposure index to the identified obsolescence issues in order to prioritize them for solution determination during the analysis phase. The tools make extensive use of the modeled system knowledge through the application of System Engineering. The application of the tools is then illustrated through a case study.

This paper proposes a novel model-based approach to technology planning and roadmapping, consisting of two complementary steps: technology forecasting and game-theoretic planning. The inherent uncertainty of target technology performances, timelines and risks impact the roadmapping process. Reducing this uncertainty is a major challenge and allows elaborating different options and scenarios. A formal methodology is proposed for quantitative forecasting in a multi-dimensional space (different performance metrics and time) based on past technology development trend information. The method adopts concepts and approaches from econometrics and is formulated as a convex optimization problem with different constraints on the frontier’s shape. It provides useful product line assessment benchmarks and helps to set reasonable goals for future technology developments. Game-theoretic planning allows addressing the strategic decisions to take, considering the technology land-scape, markets, and competition. The strategic decisions affect in turn other companies as well, which is the basis for the application of game theory, in the form of best-response functions to determine the subsequent reactions and movements of rivals in a technological landscape. The result is a simulation of a sequential game in tech-nology space, allowing evaluating possible technological development pathways and determining optimal models on the Pareto frontiers, potential targets for technology roadmapping.

International audience; Operating a modern technical system, such as a train or aircraft, calls for good organised engineering, operation and maintenance to keep the system in an optimal operational condition. Predictive maintenance is being studied and has as aim to identify errors early enough to still be able to propose a suitable solution before a real incident occurs. After all, technical problems in service may lead to delays or even interruptions of service due to extensive repair actions, such as the replacement of components. Often, predictive maintenance aims at recognising patterns in time series of monitored data and classifying these patterns as known conditions (faulty or correct). As such it provides a vital source of information for maintaining a healthy operational status. However, these approaches are still in their early phases and rely still heavily on skill and experience from the expert. In this paper, the use of self-organising maps for predictive maintenance is being discussed, applied to data of a jet engine. The aim of the study was to assess the usability of such approaches to real-life situations, assessing the learning and validation phases.

The aeronautics sector is currently living an unprecedented growth largely due to innovative projects. In several cases, such innovative developments are being carried out by Small and Medium sized-Enterprises (SMEs). For instance, in Europe, a handful of SMEs are leading projects like airships, large civil drones, or flying cars. These SMEs have all limited resources, must make strategic decisions, take considerable financial risks and in the same time must take into account the constraints of safety, cost, time and performance as any commercial organization in this industry. Moreover, today, no international regulations fully exist for the development and certification of this kind of projects. The absence of such a precise and sufficiently detailed regulatory framework requires a very close contact with regulatory instances. But, SMEs do not always have sufficient resources and internal knowledge to handle this complexity and to discuss these issues. This poses additional challenges for those SMEs that have system integration responsibilities and that must provide all the necessary means of compliance to demonstrate their ability to design, produce, and operate airships with the expected level of safety and reliability. The final objective of our research is thus to provide a methodological framework supporting SMEs in their development taking into account recent innovation and institutional rules of the sector. We aim to provide a contribution to the problematic by developing a specific Model-Based Systems Engineering (MBSE) approach. Airspace regulation, aeronautics standards and international norms on systems engineering are taken on board to be formalized in a set of models. This paper presents the on-going research project combining Systems Engineering and Project Management process modeling and taking into account the metamodeling problematic.