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Model-based diagnosis using MathModelica

In industrial processes, sudden faults must quickly be determined to avoid general failures. For this purpose model-based fault diagnosis can be used, which requires a consistent model of the real process. The powerful modelling tool MathModelica, based on the Modelica language, can be used to accomplish this. Systems for fault diagnosis could be both time-consuming and expensive if built manually. Instead, to automatically generate a fault diagnosis system, based on models built with MathModelica, would provide an efficient means of fault diagnosing. This thesis is about an algorithm which puts this into practice. MathModelica has previously not been used for fault diagnosis, which makes this a pioneering work. Therefore, the algorithm is limited to consider only static electrical circuits and to diagnose constant voltage sources and linear resistors. The algorithm takes a MathModelica model of a circuit and observations from the corresponding real system as input. Then a fault diagnosis system is generated and all possible diagnoses are obtained. The complexity of generating the diagnosis system grows very fast when the number of components is increased. Therefore, the capacity of the used computer puts limitations on the algorithm. An interesting extension would be to make the algorithm independent of the size of the circuit concerned, which could be done by considering subsets of the circuit.