Fault Detection for Heating Systems using Tensor Decompositions of Multi-linear Models

Erik Sewe, Georg Pangalos, Gerwald Lichtenberg

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 Harvard Style

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 - Volume 1: SIMULTECH, ISBN 978-989-758-265-3, pages 27-35. DOI: 10.5220/0006401400270035


in Bibtex Style

@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 - Volume 1: SIMULTECH,},
year={2017},
pages={27-35},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006401400270035},
isbn={978-989-758-265-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Fault Detection for Heating Systems using Tensor Decompositions of Multi-linear Models
SN - 978-989-758-265-3
AU - Sewe E.
AU - Pangalos G.
AU - Lichtenberg G.
PY - 2017
SP - 27
EP - 35
DO - 10.5220/0006401400270035