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Fault detection with qualitative models reduced by tensor decomposition methods

: Müller, T.; Kruppa, K.; Lichtenberg, G.; Rehault, N.


Maquin, D.:
9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015 : Paris, France, 2-4 Septtember 2015
Paris: IFAC, 2015 (IFAC PapersOnline 48.2015, Nr.21)
ISSN: 1474-6670
Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) <9, 2015, Paris>
Conference Paper
Fraunhofer ISE ()
Thermische Anlagen und Gebäudetechnik; qualitative models; Fault detection; stochastic automata; tensor decomposition; Gebäudeenergietechnik; Betriebsführung und Gesamtenergiekonzept

The paper shows how a fault diagnosis algorithm based on stochastic automata as qualitative models can be improved by tensor decomposition methods to make it applicable to complex discrete-time systems. While exponential growth of the number of transitions of the automaton with the number of states, inputs and outputs of the system can in principle not be avoided, matrix representations of the automaton can be reduced by exploiting the underlying tensor structure of the behaviour relation. For non-negative CP tensor decomposition, algorithms are available that can be tuned by defining an order of the approximation. The example of a heat exchanger shows the applicability of the proposed method in situations where real measurement data of the nominal behaviour are available and the modelling effort has to be small.