Methods and tools for model-based virtual sensors applied to condition monitoring
Abstract »Model-based virtual sensing techniques are a valuable approach to estimate system variables
which are difficult to measure. Instead of measuring these variables directly, physics-based
models and estimation algorithms are used to compute them. As the system grows in
complexity the use of dedicated modeling tools is required to reduce modeling effort and
errors. However the integration of these tools with the estimation algorithms is not always
straightforward. In this paper an overview of the main virtual sensor algorithms and the way
to connect them with modeling tools is presented. The Functional Mock-up Interface (FMI) is
discussed as the most suitable way to accomplish this. The advantages of using a symbolic
modeling language such as Modelica in the implementation of virtual sensors are also
discussed. These advantages are highlighted by means of an application example.
Biography: Mikel earned his degree in Mechanical Engineering at the University of the Basque Country in 2012. After graduation he worked as a stress engineer at Sogeclair Aerospace in Hamburg. He is currently employed as an early stage researcher at IK4-Ikerlan and is enrolled as a PhD student at KU Leuven.