Fault Detection Algorithms based on Decomposed Tensor Representations for Qualitative Models

The paper proposes two fault detection algorithms for qualitative models based on stochastic automata. We will show that storing the transition probabilities of the stochastic automaton in tensor format enables a great potential in avoiding the major limitation of the approach - the exponential growth of the number of transitions of the automaton with an increasing number of system signals. The underlying structure of the behaviour tensor of the automaton will be exploited by CP and TT decompositions which allow a reduction in the amount of data to be stored by an order of magnitude. We will provide a proof of both algorithms and show their functionality by means of a real system example.