Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
