Modern technical systems often contain components capable for extensive autonomous actions. Thus, an integrated system supervision is essential in enabling an adequate reaction for compensation of unpredictable substantial variations. This is addressed by fault detection, isolation and identification techniques discussed in this article. Therefore, an overview is given about modelling of systems subject to faults, continuous state estimation utilizing an unscented Kalman filter and hybrid state estimation by the interacting multiple model approach. These methods are generalized for application to nonlinear differential-algebraic equations, i.e. DAE systems. DAE systems arise in such fields as discretization of partial differential equations or optimization problems. However, the appearance of DAE systems most often results from an object-oriented modelling (OOM) approach. Since OOM is probably the most relevant approach for modelling complex systems, the generalization and adaptation of supervision methods to DAE is the principal subject of this contribution. Finally, the proposed fault identification approach is applied to a hydraulic system, and the related results are discussed in detail.
This contribution presents a Modelica implementation of the generalized power-based modeling approach conforming to the bond graph methodology. The corresponding developed library BondGraph is discussed in detail. It allows graphical modeling according to the bond graph formalism, and contains common bond graph elements, as well as specific nonlinear elements, especially related to hydraulic effects. Furthermore, several composed models are provided, such as switching valves, pipes, cylinders, etc. A combination with blocks of the Modelica Standard Library is possible. The application of BondGraph to an industrial plant is described to demonstrate its capabilities.
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