Methods and tools for model-based virtual sensors applied to condition monitoring

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.

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