Suchergebnisse

With the increasing level of technological developments, technical systems have become more and more complex – This complexity allows systems to perform a greater number of tasks in a more efficient and fast way -- These tasks enable the system to achieve an objective that can be related to, either the materialization of a product, the provision of a service or the satisfactory execution of a mission -- These Complex Systems (CS), can be an industrial plant or process, as well as aerospace systems, energy facilities, military industry, civil industry, aircraft, transportation, sports, etc. They all have in common, that a great amount of tasks may be automated, but anyway, they need human supervision through the so-called "Operators" (who are the qualified personnel in charge of maintaining the stability of the process) -- This operators must perform a constant monitoring and control, mainly through Human Machine Interfaces (HMI) and this Human-Machine interaction is studied by the field of Human Supervised Control (HSC) -- As CS have become more critical, they require the monitoring of more subsystems and variables, making them more susceptible to failures due to errors of the human operators -- From the literature study, it is evident that in order to avoid such errors, three aspects become relevant: i) the level of automation of the processes have a direct influence on the flow of information between the CS and the Operators, ii) the ergonomics of the graphical interfaces its critical to facilitate the interpretation of that information and iii) methodologies for a systematic CS design become necessary to guarantee tasks accomplishment -- These aspects become more critical, because CS generally integrates heterogeneous subsystems and components, which increases considerably the amount of information available to operators -- The problem is that Operators, who are experts in their disciplines, use a preferred set of data, linked to their particular knowledge (without considering the full set of variables of the whole system), to perform monitoring and control tasks -- It was also found in the literature that CS designers do not have a clear or formal guideline for selection and weighting of the relevant data. Consequently, this project proposes an information management method based on a Knowledge Management (KM), to select and weight mission data in Human Supervision Control Systems (HSC) -- The method is based on the functional analysis of the process, as well as the generation of functions from its main objective -- This method was applied in a case study, where an analysis was performed around a mission control of a solar vehicle, that compete in the Bridgestone World Solar Challenge 2015 -- It was found that the number of relevant variables to monitor the competition was small, compared to the big set of available variables -- Another finding, was that the set of relevant variables is strongly influenced by the Operating States (OS) of the vehicle throughout the different moments of the competition -- Although there are some variables that are consistently stronger than others in all OS, in general, the variables' importance presents a variable behavior between OS, concluding that the relevance of the variables is dynamic