MOUZINHO, L. F.; http://lattes.cnpq.br/2957679737443669; MOUZINHO, Lucilene Ferreira
Resumo:
Indirect measuring is an alternative wherever is difficult to perform a measuring by
means of sensors directly associated with variations in the magnitude to be measured.
This work aims at developing procedures for indirect measurement systems, whether
this measuring is done in real time or not. Indirect Measurement Systems (IMS) are
classified in this Thesis in such a way as to specify the sort of treatment to be used for
the attainment of a given magnitude, also including the means to analyze those results.
Each method must be used according to the application, whether isolated or in groups,
as to obtain the magnitude of interest. In this thesis, two techniques used in case studies
are approached: the Kalman Filter and the Artificial Neural Network. Considering the
complexity of the applications performed in case studies, it is crucial to develop methods
addressed to the application as well as simulation and prior analysis of the results until
the system’s implementation. Four IMS are developed: indirect speed measuring of
an space aircraft; indirect temperature measuring of an object inside an oven; indirect
rotor speed measuring of an induction motor and, as supplement to this case study,
the robust indirect speed measuring. The IMS implementation allows us to check the
viability of using mathematical tools to conduct indirect optimal measurements, both,
stochastic and neuronal in dynamic systems.