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Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality

: Kühnert, Christian; Frey, Christian; Seyboldt, Ruben

Fulltext urn:nbn:de:0011-n-5320114 (408 KByte PDF)
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Created on: 8.2.2019

Beyerer, J.:
Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2018 : Selected papers from the International Conference ML4CPS 2018, Karlsruhe, October 23rd and 24th, 2018
Berlin: Springer Vieweg, 2019 (Technologies for Intelligent Automation 9)
ISBN: 978-3-662-58484-2 (Print)
ISBN: 978-3-662-58485-9 (Online)
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <4, 2018, Karlsruhe>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
Spectral Granger Causality; Detection; Directed Connectivities; time Series Analysis

Industrial plants usually consist of different process units
which are strongly cross-linked to each other. This leads to the point
that a voluntary or involuntary change in one unit (e.g. changing some
process control parameter or having a malfunctioning value) can lead to
unexpected results in another process unit. Hence, knowing which are the
causing and which are the effecting process variables is of great interest.
Still, depending on the underlying process and the characteristics of the
excitation signal, directed connectivities can or can not be detected.
Therefore, in this paper several types of dynamic SISO systems and
excitation signals are defined for which a directed connectivity from input
to output signal should be detected and from output to input should not
be detected. As a method for the detection of directed influences Spectral
Granger Causality is used, which has been extended with a surrogatebased
significance test. This test is used to define if a directed influence
exists from one process variable to another.