Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Fault detection for heating systems using tensor decompositions of multilinear models
 Rango, F. de ; Institute for Systems and Technologies of Information, Control and Communication INSTICC, Setubal: 7th International Conference on Simulation and Modeling Methodologies, Technologies and Application, SIMULTECH 2017. Proceedings : July 2628, 2017, in Madrid, Spain SciTePress, 2017 ISBN: 9789897582653 S.2735 
 International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH) <7, 2017, Madrid> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer ISIT () 
Abstract
A modelbased fault detection method for heating systems is proposed. Two examples of heating system units are under investigation. These systems can be represented as multilinear systems. Subspace identification methods are used to identify linear timeinvariant models for each operating regime, resulting in a parameter tensor. In case of missing data and models for some operating regimes, an approximation method is proposed, where the canonical polyadic tensor decomposition method is used. Low rank approximations are found using an algorithm specialized for incomplete tensors. The tensor of these approximations defines the models in operating regimes, where no measurements were available. Fault detection is done using parity equations and application examples using real measurement data of a heat generation unit are given.