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Authors: Erik Sewe 1 ; Georg Pangalos 2 and Gerwald Lichtenberg 3

Affiliations: 1 PLENUM Ingenieurgesellschaft für Planung Energie Umwelt mbH, Germany ; 2 Fraunhofer Institute for Silicon Technology ISIT and Application Center Power Electronics for Renewable Energy Systems, Germany ; 3 Hamburg University of Applied Sciences, Germany

Keyword(s): Fault Detection, Boilers, Heating Systems, Multi-linear System, Tensor Representation.

Related Ontology Subjects/Areas/Topics: Dynamical Systems Models and Methods ; Formal Methods ; Non-Linear Systems ; Simulation and Modeling

Abstract: A model-based fault detection method for heating systems is proposed. Two examples of heating system units are under investigation. These systems can be represented as multi-linear systems. Subspace identification methods are used to identify linear time-invariant 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.

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Paper citation in several formats:
Sewe, E.; Pangalos, G. and Lichtenberg, G. (2017). Fault Detection for Heating Systems using Tensor Decompositions of Multi-linear Models. In Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-265-3; ISSN 2184-2841, SciTePress, pages 27-35. DOI: 10.5220/0006401400270035

@conference{simultech17,
author={Erik Sewe. and Georg Pangalos. and Gerwald Lichtenberg.},
title={Fault Detection for Heating Systems using Tensor Decompositions of Multi-linear Models},
booktitle={Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2017},
pages={27-35},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006401400270035},
isbn={978-989-758-265-3},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Fault Detection for Heating Systems using Tensor Decompositions of Multi-linear Models
SN - 978-989-758-265-3
IS - 2184-2841
AU - Sewe, E.
AU - Pangalos, G.
AU - Lichtenberg, G.
PY - 2017
SP - 27
EP - 35
DO - 10.5220/0006401400270035
PB - SciTePress

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