Abstract
The paper shows how a fault detection algorithm based on stochastic automata as qualitative model can be improved by non-negative CP tensor decomposition to make it applicable to large discrete-time systems. Because exponential growth of the number of transitions of the automaton with a rising number of states, inputs and outputs of the system can usually not be avoided, tensor decomposition methods enable the reduction of the amount of data to be stored by an order of magnitude. In order to exploit the full potential of the decomposition, a fault detection algorithm that is applicable to the decomposed tensor structure is defined. An example based on real measurement data shows the functionality of the algorithm.
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